integrations/integrate_dataprofiler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# `YData Synthetic` and `Data Profiler`: Streamlined data synthesis and profiling\n",
    "\n",
    "`YData Synthetic` provides tools for quickly setting up a data synthesis project, trying out different models, datasets, and parameterizations. It is easy to get lost in the process of experimentation and model tweaking but, at the end of the day, quality assessment is extremely important and should not be disregarded, we need to validate that the synthetic data is suitable for our purposes, right?\n",
    "\n",
    "When assessing the quality of the synthetic outputs we focus on two main aspects:\n",
    "\n",
    "* Privacy, does the generated data leak any sensitive information? (Names, addresses, combinations of features that can make a real-world record identifiable)\n",
    "* Utility, how well does the synthetic data preserve the original data properties? (Marginal distributions, correlations)\n",
    "\n",
    "There are no simple ways of doing this important part of the work from scratch, that is why we are showing you today a good addition to your toolbox! This notebook shows you how to integrate the [Data Profiler](https://github.com/capitalone/DataProfiler) package on a `YData Synthetic` project. By combining these two packages you can easily synthesize data and profile it to assess the quality of the generated data.\n",
    "\n",
    "The demo will take the following steps:\n",
    "\n",
    "1. Install `DataProfiler` and import required packages\n",
    "2. Read a dataset\n",
    "3. Profile the dataset\n",
    "4. Data processing\n",
    "5. Define and fit a synthesizer\n",
    "6. Sample and post-process synthetic data\n",
    "7. Profiling synthetic data and comparing samples\n",
    "8. Saving/loading a profile for later analysis\n",
    "\n",
    "By the end of the notebook we will have covered basic `DataProfiler` usage in a data synthesis flow. We will focus the data pre-processing and its impact on the utility of the produced samples. `DataProfiler` will provide us with the necessary inputs to understand what pre-processing leads to the best results.\n",
    "\n",
    "That's enough for an introduction, let's get started!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install `DataProfiler` and import required packages\n",
    "\n",
    "`DataProfiler` is a package that helps you do data analysis with a focus on profiling and sensitive data detection. In this demo we will use the slimmer version and leverage Profiler reports and graphical utilities, we will focus on the utility aspect.\n",
    "\n",
    "To install the package we just need to uncomment and run the following cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install DataProfiler[reports]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we import all the required packages for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from pandas import read_csv, cut, DataFrame\n",
    "from numpy.random import randint, normal\n",
    "from IPython.display import display\n",
    "from sklearn.decomposition import NMF\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from ydata_synthetic.synthesizers.regular import WGAN_GP\n",
    "from ydata_synthetic.synthesizers import ModelParameters, TrainParameters\n",
    "from ydata_synthetic.preprocessing.regular.processor import RegularDataProcessor\n",
    "from dataprofiler import Profiler, graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using GPU you might need to allow memory growth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import config\n",
    "physical_devices = config.list_physical_devices('GPU') \n",
    "for device in physical_devices:\n",
    "    config.experimental.set_memory_growth(device, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Read a dataset\n",
    "\n",
    "The dataset that we will use in this demo is sampled from the popular [cardio](https://www.kaggle.com/sulianova/cardiovascular-disease-dataset).\n",
    "\n",
    "We produced this sample by cleaning out some outliers that are extremely unlikely or even physically impossible by enforcing the following conditions:\n",
    "\n",
    "* Diastolic (lower) pressure must be bigger than 0\n",
    "* Systolic pressure (higher) must not be bigger than 240 (above [hypertensive crisis](https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings))\n",
    "* Diastolic pressure must be lower than the systolic blood pressure values\n",
    "\n",
    "By applying these criteria we filtered out roughly 1.9% of the records and made the sample much more representative of a real population.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>smoke</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
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      ],
      "text/plain": [
       "     age  gender  height  weight  ap_hi  ap_lo  cholesterol  gluc  smoke  \\\n",
       "0  18393       2     168    62.0    110     80            1     1      0   \n",
       "1  20228       1     156    85.0    140     90            3     1      0   \n",
       "2  18857       1     165    64.0    130     70            3     1      0   \n",
       "3  17623       2     169    82.0    150    100            1     1      0   \n",
       "4  17474       1     156    56.0    100     60            1     1      0   \n",
       "\n",
       "   alco  active  cardio  \n",
       "0     0       1       0  \n",
       "1     0       1       1  \n",
       "2     0       0       1  \n",
       "3     0       1       1  \n",
       "4     0       0       0  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = read_csv('../../data/cardio_sample.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Profile the dataset\n",
    "To quickly learn more about our data, we can have a look at what `DataProfiler` can provide.\n",
    "\n",
    "The `Profiler` is the main class that unlocks data analysis.\n",
    "\n",
    "Depending on the passed data object either an `UnstructuredProfiler`, specialized for text data, or a `StructuredProfiler`, specialized for tabular data, will be automatically dispatched.\n",
    "\n",
    "Creating a `Profiler` for the smoker sample is simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "smoker_data = data.loc[data.smoke == 1].drop(columns='smoke')\n",
    "smoker_profile = Profiler(smoker_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a lot of information to unpack from this object, we will focus on what is most informative to our task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'column_count': 11,\n",
      " 'correlation_matrix': None,\n",
      " 'duplicate_row_count': 0,\n",
      " 'encoding': None,\n",
      " 'file_type': \"<class 'pandas.core.frame.DataFrame'>\",\n",
      " 'profile_schema': defaultdict(<class 'list'>,\n",
      "                               {'active': [9],\n",
      "                                'age': [0],\n",
      "                                'alco': [8],\n",
      "                                'ap_hi': [4],\n",
      "                                'ap_lo': [5],\n",
      "                                'cardio': [10],\n",
      "                                'cholesterol': [6],\n",
      "                                'gender': [1],\n",
      "                                'gluc': [7],\n",
      "                                'height': [2],\n",
      "                                'weight': [3]}),\n",
      " 'row_count': 6041,\n",
      " 'row_has_null_ratio': 0.0,\n",
      " 'row_is_null_ratio': 0.0,\n",
      " 'samples_used': 5000,\n",
      " 'times': {'row_stats': 0.0022242069244384766},\n",
      " 'unique_row_ratio': 1.0}\n"
     ]
    }
   ],
   "source": [
    "base_report = smoker_profile.report()\n",
    "pprint({k:v for k,v in base_report['global_stats'].items() if k!='chi2_matrix'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the global part of the report we can see that we have no duplicated rows, no missing values and also we get some general information about the dataset schema.\n",
    "\n",
    "Despite not being necessary, in this case, a complete picture on missing data could be obtained with the `Graph` module's `plot_missing_values_matrix` method. It's output lets us know the prevalence of missing values, at which columns they occur and what are the inferred missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(graphs.plot_missing_values_matrix(smoker_profile))\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The blue bars mean we have no missing data.\n",
    "\n",
    "Lets look at the feature level information and extract some more knowledge about the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'age': {'categorical': False,\n",
      "         'column_name': 'age',\n",
      "         'data_label': 'INTEGER',\n",
      "         'data_type': 'int',\n",
      "         'order': 'random',\n",
      "         'samples': ['19890', '21809', '19714', '22499', '23199'],\n",
      "         'statistics': {'kurtosis': -0.9818219509492989,\n",
      "                        'max': 23687.0,\n",
      "                        'mean': 19076.8908,\n",
      "                        'median': 19125.727499999997,\n",
      "                        'median_abs_deviation': 1992.522916666669,\n",
      "                        'min': 14292.0,\n",
      "                        'mode': [18214.4125],\n",
      "                        'null_count': 0,\n",
      "                        'null_types': [],\n",
      "                        'null_types_index': {},\n",
      "                        'num_negatives': 0,\n",
      "                        'num_zeros': 0,\n",
      "                        'quantiles': {0: 16960.179999999993,\n",
      "                                      1: 19125.727499999997,\n",
      "                                      2: 21098.090312499997},\n",
      "                        'sample_size': 5000,\n",
      "                        'skewness': -0.1399094787846943,\n",
      "                        'stddev': 2498.6529493684466,\n",
      "                        'sum': 95384454.0,\n",
      "                        'unique_count': 3449,\n",
      "                        'unique_ratio': 0.6898,\n",
      "                        'variance': 6243266.561387637}},\n",
      " 'ap_hi': {'categorical': True,\n",
      "           'column_name': 'ap_hi',\n",
      "           'data_label': 'INTEGER',\n",
      "           'data_type': 'int',\n",
      "           'order': 'random',\n",
      "           'samples': ['150', '160', '120', '130', '120'],\n",
      "           'statistics': {'gini_impurity': 0.8049908000000006,\n",
      "                          'kurtosis': 1.7775927622583185,\n",
      "                          'max': 240.0,\n",
      "                          'mean': 127.9986,\n",
      "                          'median': 120.14309232480534,\n",
      "                          'median_abs_deviation': 9.91255013329521,\n",
      "                          'min': 80.0,\n",
      "                          'mode': [120.08],\n",
      "                          'null_count': 0,\n",
      "                          'null_types': [],\n",
      "                          'null_types_index': {},\n",
      "                          'num_negatives': 0,\n",
      "                          'num_zeros': 0,\n",
      "                          'quantiles': {0: 120.03185761957731,\n",
      "                                        1: 120.14309232480534,\n",
      "                                        2: 140.0531914893617},\n",
      "                          'sample_size': 5000,\n",
      "                          'skewness': 0.9194316191166618,\n",
      "                          'stddev': 17.353063816074698,\n",
      "                          'sum': 639993.0,\n",
      "                          'unalikeability': 0.8051518303660732,\n",
      "                          'unique_count': 59,\n",
      "                          'unique_ratio': 0.0118,\n",
      "                          'variance': 301.12882380476094}},\n",
      " 'ap_lo': {'categorical': True,\n",
      "           'column_name': 'ap_lo',\n",
      "           'data_label': 'INTEGER',\n",
      "           'data_type': 'int',\n",
      "           'order': 'random',\n",
      "           'samples': ['90', '80', '80', '80', '70'],\n",
      "           'statistics': {'gini_impurity': 0.6898387200000002,\n",
      "                          'kurtosis': 1.8236926682235512,\n",
      "                          'max': 140.0,\n",
      "                          'mean': 81.9478,\n",
      "                          'median': 80.0204149291076,\n",
      "                          'median_abs_deviation': 9.914667536170178,\n",
      "                          'min': 45.0,\n",
      "                          'mode': [80.00750000000001],\n",
      "                          'null_count': 0,\n",
      "                          'null_types': [],\n",
      "                          'null_types_index': {},\n",
      "                          'num_negatives': 0,\n",
      "                          'num_zeros': 0,\n",
      "                          'quantiles': {0: 79.97089449541285,\n",
      "                                        1: 80.0204149291076,\n",
      "                                        2: 89.96213107638889},\n",
      "                          'sample_size': 5000,\n",
      "                          'skewness': 0.40637597887931454,\n",
      "                          'stddev': 9.768938611349672,\n",
      "                          'sum': 409739.0,\n",
      "                          'unalikeability': 0.6899767153430686,\n",
      "                          'unique_count': 46,\n",
      "                          'unique_ratio': 0.0092,\n",
      "                          'variance': 95.43216159231847}}}\n"
     ]
    }
   ],
   "source": [
    "def summarize_data_stats(data_stats, show_feats = None):\n",
    "    \"This function will help us preserving only the most indicative elements of the report in our analysis.\"\n",
    "    summarized = {}\n",
    "    stats_exclude_keys = ['avg_predictions', 'data_label_representation', 'data_type_representation', 'histogram', 'times', 'precision', 'label_representation', 'categorical_count', 'categories', 't-test']\n",
    "    for feat_info in data_stats:\n",
    "        name = feat_info['column_name']\n",
    "        if show_feats and name not in show_feats:\n",
    "            continue\n",
    "        stats = {k: v for k, v in feat_info.pop('statistics').items() if k not in stats_exclude_keys}\n",
    "        feat_info['statistics'] = stats\n",
    "        summarized[name] = feat_info\n",
    "    return summarized\n",
    "\n",
    "# Looking at some feature\n",
    "show_feats = ['age','ap_lo', 'ap_hi']\n",
    "\n",
    "pprint(summarize_data_stats(base_report['data_stats'], show_feats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unpacking some of what we can see from this report:\n",
    "\n",
    "* Age seems to be provided as days\n",
    "* There are a lot of unique values for the age feature but, according to the descriptive statistics, it is not uniform or normal distributed\n",
    "* According to the samples values of ap_hi and ap_lo, this feature seems to be grouped as 10's. However the number of unique values suggests the opposite\n",
    "\n",
    "These are some useful insights for us to consider when preprocessing the data.\n",
    "\n",
    "From the `Graph` module we can also get a glimpse of the empirical distributions of the features with `plot_histograms`.\n",
    "\n",
    "These methods return `matplotlib` figures which also allows you to do some further customization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "marginals = graphs.plot_histograms(smoker_profile)\n",
    "marginals.suptitle('Smoker data')\n",
    "marginals.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we also know the following:\n",
    "\n",
    "* Age, ap_hi and ap_lo seem to exhibit a strong multimodal behavior. We might want to discretize these features\n",
    "* Height and weight seem almost normal, but right-tailed, they can probably be reasonably well fit with a log-normal distribution\n",
    "\n",
    "We will take these insights for the data pre-processing.\n",
    "\n",
    "## 4. Data preprocessing\n",
    "We have detected some feature distributions that probably will be hard to pick up by the GANs. We will do a custom pre-processing flow to try to concentrate the information of our features so that the data relationships can be more easily picked-up.\n",
    "\n",
    "Starting with some feature engineering:\n",
    "* For the age we will convert days to years and bin them in ranges of 5\n",
    "* Relative to the blood pressure values, rounding to nearest 10's should also provide decent results, we have noticed that most of the values are actually already in this format. Also it is similar to way physicians read arterial pressure values on checkup consultations!\n",
    "\n",
    "This preprocessing flow will leave us with a lot of categorical features. Categorical distributions are [tricky to capture](https://github.com/ydataai/ydata-synthetic/blob/dev/examples/regular/gumbel_softmax_example.ipynb) as well. In order to avoid modelling categorical distributions we will pass a dense representation of the data to the GANs. Some dimensionality reduction techniques from `Scikit learn` are suitable to obtaining a dense representation of the data.\n",
    "To test the hypothesis that this additional processing step helps in GAN learning, we will split our flow in two and create two training datasets:\n",
    "1. Only pre-processing the features as explained above\n",
    "2. Applying a dimensionality reduction technique to the output of step one.\n",
    "\n",
    "### 4.1 - Feature Engineering\n",
    "#### Age feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N1b59+/Tiiy9qxowZam5u1pkzZzxmd2praxUfHy9Jio+Pv+DX1tuf1mrvczE2m002m61TdQMAgJ7Bq7DT2tqq0tJSlZWVqa6uTm1tbR7Hd+7c6XVBbW1tampqUmpqqkJDQ1VWVqacnBxJUmVlpaqqquRwOCRJDodDTz/9tOrq6hQXFydJ2r59uyIjI5WSkuJ1DQAAwBxehZ05c+aotLRUWVlZGjVqlIKCgrz68KKiIk2dOlVDhgzR2bNntWHDBu3atUvvvfeeoqKilJ+fr8LCQsXExCgyMlIPP/ywHA6HJkyYIEmaPHmyUlJSdO+992r58uVyOp1asGCBCgoKmLkBAACSvAw7Gzdu1Ouvv65p06Z16sPr6uo0c+ZM1dTUKCoqSmPGjNF7772nX/7yl5Kk559/XsHBwcrJyVFTU5MyMzO1atUq9+t79eqlzZs3a9asWXI4HOrbt6/y8vK0ePHiTtUFAADM4fUC5fZ1Np3R0Q+F9u7dW8XFxSouLr5kn6SkJL3zzjudrgUAAJjJq+/ZefTRR/Xiiy/Ksixf1wMAAOBTXs3s/M///I/ef/99vfvuu7rhhhsUGhrqcfyNN97wSXEAAACd5VXYiY6O1h133OHrWgAAAHzOq7BTUlLi6zoAAAC6hFdrdiTp/Pnz2rFjh1566SWdPXtWklRdXa2GhgafFQcAANBZXs3s/OUvf9GUKVNUVVWlpqYm/fKXv1RERISeffZZNTU1ac2aNb6uEwAAwCtezezMmTNH48eP13fffac+ffq42++44w6VlZX5rDgAAIDO8mpm57//+7+1Z88ehYWFebQPHTpUX3/9tU8KAwAA8AWvZnba2trU2tp6QfupU6cUERHR6aIAAAB8xauwM3nyZL3wwgvu/aCgIDU0NGjRokWd/gkJAAAAX/LqNtZzzz2nzMxMpaSkqLGxUXfffbeOHj2qAQMG6D/+4z98XSMAAIDXvAo7gwcP1meffaaNGzfq4MGDamhoUH5+vnJzcz0WLAMAAPibV2FHkkJCQnTPPff4spa/WUMf39Jhn6+WZXVDJQAAmMersLN+/frLHp85c6ZXxQAAAPiaV2Fnzpw5HvstLS36/vvvFRYWpvDwcMIOAAAIGF49jfXdd995bA0NDaqsrNTEiRNZoAwAAAKK17+N9VPDhg3TsmXLLpj1AQAA8CefhR3ph0XL1dXVvnxLAACATvFqzc6f//xnj33LslRTU6N/+7d/04033uiTwgAAAHzBq7Bz++23e+wHBQVp4MCBmjRpkp577jlf1AUAAOATXoWdtrY2X9cBAADQJXy6ZgcAACDQeDWzU1hYeMV9V6xY4c1HAAAA+IRXYefTTz/Vp59+qpaWFl1//fWSpC+++EK9evXSuHHj3P2CgoJ8UyUAAICXvAo7t912myIiIrRu3Tr1799f0g9fNHj//ffrpptu0qOPPurTIgEAALzl1Zqd5557TkuXLnUHHUnq37+/nnrqKZ7GAgAAAcWrsONyufTNN99c0P7NN9/o7NmznS4KAADAV7wKO3fccYfuv/9+vfHGGzp16pROnTql//qv/1J+fr6ys7N9XSMAAIDXvFqzs2bNGs2bN0933323WlpafnijkBDl5+fr97//vU8LBAAA6Ayvwk54eLhWrVql3//+9zp+/Lgk6dprr1Xfvn19WhwAAEBndepLBWtqalRTU6Nhw4apb9++sizLV3UBAAD4hFdh59tvv1V6erqGDx+uadOmqaamRpKUn5/PY+cAACCgeBV25s6dq9DQUFVVVSk8PNzdPmPGDG3dutVnxQEAAHSWV2t2tm3bpvfee0+DBw/2aB82bJj+8pe/+KQwAAAAX/Aq7Jw7d85jRqfd6dOnZbPZOl0UAADwv6GPb+mwz1fLsrqhks7x6jbWTTfdpPXr17v3g4KC1NbWpuXLl+vWW2/1WXEAAACd5dXMzvLly5Wenq79+/erublZv/nNb3T48GGdPn1aH374oa9rBAAA8JpXMzujRo3SF198oYkTJ2r69Ok6d+6csrOz9emnn+raa6/1dY0AAABeu+qZnZaWFk2ZMkVr1qzRv/zLv3RFTQAAAD5z1TM7oaGhOnjwYFfUAgAA4HNe3ca65557tHbtWl/XAgAA4HNeLVA+f/68XnnlFe3YsUOpqakX/CbWihUrfFIcAABAZ11V2Pnyyy81dOhQff755xo3bpwk6YsvvvDoExQU5LvqAAAAOumqws6wYcNUU1Oj999/X9IPPw+xcuVK2e32LikOAACgs65qzc5Pf9X83Xff1blz53xaEAAAgC95tUC53U/DDwAAQKC5qrATFBR0wZoc1ugAAIBAdlVrdizL0n333ef+sc/GxkY99NBDFzyN9cYbb/iuQgAAgE64qrCTl5fnsX/PPff4tBgAAABfu6qwU1JS0lV1AAAAdIlOLVAGAAAIdIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACj+TXsLF26VH//93+viIgIxcXF6fbbb1dlZaVHn8bGRhUUFCg2Nlb9+vVTTk6OamtrPfpUVVUpKytL4eHhiouL0/z583X+/PnuPBUAABCg/Bp2du/erYKCAu3du1fbt29XS0uLJk+e7PFL6nPnztXbb7+tTZs2affu3aqurlZ2drb7eGtrq7KystTc3Kw9e/Zo3bp1Ki0t1cKFC/1xSgAAIMBc1Tco+9rWrVs99ktLSxUXF6eKigrdfPPNqq+v19q1a7VhwwZNmjRJ0g/f4jxy5Ejt3btXEyZM0LZt23TkyBHt2LFDdrtdY8eO1ZIlS/TYY4/pySefVFhYmD9ODQAABIiAWrNTX18vSYqJiZEkVVRUqKWlRRkZGe4+I0aM0JAhQ1ReXi5JKi8v1+jRo2W32919MjMz5XK5dPjw4Yt+TlNTk1wul8cGAADMFDBhp62tTY888ohuvPFGjRo1SpLkdDoVFham6Ohoj752u11Op9Pd58dBp/14+7GLWbp0qaKiotxbYmKij88GAAAEioAJOwUFBfr888+1cePGLv+soqIi1dfXu7eTJ092+WcCAAD/8OuanXazZ8/W5s2b9cEHH2jw4MHu9vj4eDU3N+vMmTMeszu1tbWKj4939/n444893q/9aa32Pj9ls9lks9l8fBYAACAQ+XVmx7IszZ49W3/605+0c+dOJScnexxPTU1VaGioysrK3G2VlZWqqqqSw+GQJDkcDh06dEh1dXXuPtu3b1dkZKRSUlK650QAAEDA8uvMTkFBgTZs2KC33npLERER7jU2UVFR6tOnj6KiopSfn6/CwkLFxMQoMjJSDz/8sBwOhyZMmCBJmjx5slJSUnTvvfdq+fLlcjqdWrBggQoKCpi9AQAA/g07q1evliTdcsstHu0lJSW67777JEnPP/+8goODlZOTo6amJmVmZmrVqlXuvr169dLmzZs1a9YsORwO9e3bV3l5eVq8eHF3nQYAAAhgfg07lmV12Kd3794qLi5WcXHxJfskJSXpnXfe8WVpAADAEAHzNBYAAEBXIOwAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKMRdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0UL8XQAABIKhj2/psM9Xy7K6oRIAvkbYAYAARPgCfIfbWAAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARuPRcwBAh3gUHj0ZMzsAAMBozOwACFjMJgDwBWZ2AACA0Qg7AADAaH4NOx988IFuu+02DRo0SEFBQXrzzTc9jluWpYULFyohIUF9+vRRRkaGjh496tHn9OnTys3NVWRkpKKjo5Wfn6+GhoZuPAsAABDI/Bp2zp07p5/97GcqLi6+6PHly5dr5cqVWrNmjT766CP17dtXmZmZamxsdPfJzc3V4cOHtX37dm3evFkffPCBHnzwwe46BQAAEOD8ukB56tSpmjp16kWPWZalF154QQsWLND06dMlSevXr5fdbtebb76pO++8U//7v/+rrVu3at++fRo/frwk6V//9V81bdo0/eEPf9CgQYO67VwAAEBgCtg1OydOnJDT6VRGRoa7LSoqSmlpaSovL5cklZeXKzo62h10JCkjI0PBwcH66KOPur1mAAAQeAL20XOn0ylJstvtHu12u919zOl0Ki4uzuN4SEiIYmJi3H0upqmpSU1NTe59l8vlq7IBAECACdiZna60dOlSRUVFubfExER/lwQAALpIwIad+Ph4SVJtba1He21trftYfHy86urqPI6fP39ep0+fdve5mKKiItXX17u3kydP+rh6AAAQKAL2NlZycrLi4+NVVlamsWPHSvrhdtNHH32kWbNmSZIcDofOnDmjiooKpaamSpJ27typtrY2paWlXfK9bTabbDZbl58D0NPwjcUATOTXsNPQ0KBjx46590+cOKEDBw4oJiZGQ4YM0SOPPKKnnnpKw4YNU3Jysp544gkNGjRIt99+uyRp5MiRmjJlih544AGtWbNGLS0tmj17tu68806exAIAAJL8HHb279+vW2+91b1fWFgoScrLy1Npaal+85vf6Ny5c3rwwQd15swZTZw4UVu3blXv3r3dr3nttdc0e/ZspaenKzg4WDk5OVq5cmW3nwsAoPswC4mr4dewc8stt8iyrEseDwoK0uLFi7V48eJL9omJidGGDRu6ojwAAGCAgF2zA/ga/08QAP42EXYMwh9zAAAuFLCPngMAAPgCYQcAABiNsAMAAIzGmh0AADqJNZOBjZkdAABgNMIOAAAwGmEHAAAYjTU76DLcwwYABAJmdgAAgNEIOwAAwGiEHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARuN7dgA/4XuIAKB7MLMDAACMRtgBAABGI+wAAACjsWYHAADDsCbQEzM7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0fi6ii13JV3YDAICuw8wOAAAwGmEHAAAYjbADAACMRtgBAABGI+wAAACjEXYAAIDRCDsAAMBohB0AAGA0wg4AADAaYQcAABiNsAMAAIxG2AEAAEYj7AAAAKPxq+cAAMBrQx/f0mGfr5ZldUMll8bMDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYzZiwU1xcrKFDh6p3795KS0vTxx9/7O+SAABAADAi7Pzxj39UYWGhFi1apE8++UQ/+9nPlJmZqbq6On+XBgAA/MyIsLNixQo98MADuv/++5WSkqI1a9YoPDxcr7zyir9LAwAAftbjf/W8ublZFRUVKioqcrcFBwcrIyND5eXlF31NU1OTmpqa3Pv19fWSJJfL5fP62pq+98n7XEltV/JZvI957xOINfXE97kSgVYz78P7BML7XImu+Pv64/e1LOvyHa0e7uuvv7YkWXv27PFonz9/vvWLX/zioq9ZtGiRJYmNjY2NjY3NgO3kyZOXzQo9fmbHG0VFRSosLHTvt7W16fTp04qNjVVQUJAfK7s4l8ulxMREnTx5UpGRkf4uxxiMa9dhbLsOY9s1GNeu05Vja1mWzp49q0GDBl22X48POwMGDFCvXr1UW1vr0V5bW6v4+PiLvsZms8lms3m0RUdHd1WJPhMZGcl/hF2Ace06jG3XYWy7BuPadbpqbKOiojrs0+MXKIeFhSk1NVVlZWXutra2NpWVlcnhcPixMgAAEAh6/MyOJBUWFiovL0/jx4/XL37xC73wwgs6d+6c7r//fn+XBgAA/MyIsDNjxgx98803WrhwoZxOp8aOHautW7fKbrf7uzSfsNlsWrRo0QW33tA5jGvXYWy7DmPbNRjXrhMIYxtkWR09rwUAANBz9fg1OwAAAJdD2AEAAEYj7AAAAKMRdgAAgNEIOwFo2bJlCgoK0iOPPOJua2xsVEFBgWJjY9WvXz/l5ORc8EWK6NjFxvaWW25RUFCQx/bQQw/5r8ge4sknn7xg3EaMGOE+zjXrvY7Glmu2c77++mvdc889io2NVZ8+fTR69Gjt37/ffdyyLC1cuFAJCQnq06ePMjIydPToUT9W3DN0NK733XffBdftlClTuqU2Ix49N8m+ffv00ksvacyYMR7tc+fO1ZYtW7Rp0yZFRUVp9uzZys7O1ocffuinSnueS42tJD3wwANavHixez88PLw7S+uxbrjhBu3YscO9HxLy//+TwjXbOZcbW4lr1lvfffedbrzxRt1666169913NXDgQB09elT9+/d391m+fLlWrlypdevWKTk5WU888YQyMzN15MgR9e7d24/VB64rGVdJmjJlikpKStz73fU4OmEngDQ0NCg3N1cvv/yynnrqKXd7fX291q5dqw0bNmjSpEmSpJKSEo0cOVJ79+7VhAkT/FVyj3GpsW0XHh5+yZ8XwaWFhIRcdNy4ZjvvUmPbjmvWO88++6wSExM9/uAmJye7/21Zll544QUtWLBA06dPlyStX79edrtdb775pu68885ur7kn6Ghc29lsNr9ct9zGCiAFBQXKyspSRkaGR3tFRYVaWlo82keMGKEhQ4aovLy8u8vskS41tu1ee+01DRgwQKNGjVJRUZG+//77bq6wZzp69KgGDRqka665Rrm5uaqqqpLENesLlxrbdlyz3vnzn/+s8ePH6x//8R8VFxenn//853r55Zfdx0+cOCGn0+lx7UZFRSktLY1r9zI6Gtd2u3btUlxcnK6//nrNmjVL3377bbfUx8xOgNi4caM++eQT7du374JjTqdTYWFhF/xYqd1ul9Pp7KYKe67Lja0k3X333UpKStKgQYN08OBBPfbYY6qsrNQbb7zRzZX2LGlpaSotLdX111+vmpoa/e53v9NNN92kzz//nGu2ky43thEREVyznfDll19q9erVKiws1G9/+1vt27dPv/71rxUWFqa8vDz39fnTb+Dn2r28jsZV+uEWVnZ2tpKTk3X8+HH99re/1dSpU1VeXq5evXp1aX2EnQBw8uRJzZkzR9u3b+d+sI9dydg++OCD7n+PHj1aCQkJSk9P1/Hjx3Xttdd2V6k9ztSpU93/HjNmjNLS0pSUlKTXX39dffr08WNlPd/lxjY/P59rthPa2to0fvx4PfPMM5Kkn//85/r888+1Zs0a9x9lXL0rGdcf3wIcPXq0xowZo2uvvVa7du1Senp6l9bHbawAUFFRobq6Oo0bN04hISEKCQnR7t27tXLlSoWEhMhut6u5uVlnzpzxeF1tbS337DvQ0di2trZe8Jq0tDRJ0rFjx7q73B4tOjpaw4cP17FjxxQfH88160M/HtuL4Zq9cgkJCUpJSfFoGzlypPs2Yfv1+dMnB7l2L6+jcb2Ya665RgMGDOiW65awEwDS09N16NAhHThwwL2NHz9eubm57n+HhoaqrKzM/ZrKykpVVVXJ4XD4sfLA19HYXmzq9MCBA5J++I8XV66hoUHHjx9XQkKCUlNTuWZ96MdjezFcs1fuxhtvVGVlpUfbF198oaSkJEk/LKqNj4/3uHZdLpc++ugjrt3L6GhcL+bUqVP69ttvu+e6tRCQ/uEf/sGaM2eOe/+hhx6yhgwZYu3cudPav3+/5XA4LIfD4b8Ce7Afj+2xY8esxYsXW/v377dOnDhhvfXWW9Y111xj3Xzzzf4tsgd49NFHrV27dlknTpywPvzwQysjI8MaMGCAVVdXZ1kW12xnXG5suWY75+OPP7ZCQkKsp59+2jp69Kj12muvWeHh4darr77q7rNs2TIrOjraeuutt6yDBw9a06dPt5KTk62//vWvfqw8sHU0rmfPnrXmzZtnlZeXWydOnLB27NhhjRs3zho2bJjV2NjY5fURdgLUT8POX//6V+tXv/qV1b9/fys8PNy64447rJqaGv8V2IP9eGyrqqqsm2++2YqJibFsNpt13XXXWfPnz7fq6+v9W2QPMGPGDCshIcEKCwuz/u7v/s6aMWOGdezYMfdxrlnvXW5suWY77+2337ZGjRpl2Ww2a8SIEda///u/exxva2uznnjiCctut1s2m81KT0+3Kisr/VRtz3G5cf3++++tyZMnWwMHDrRCQ0OtpKQk64EHHrCcTme31BZkWZbV9fNHAAAA/sGaHQAAYDTCDgAAMBphBwAAGI2wAwAAjEbYAQAARiPsAAAAoxF2AACA0Qg7AADAaIQdAABgNMIOAAAwGmEHAAAYjbADAACM9n9gNfZHmyNAwwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train1 = smoker_data.copy()\n",
    "\n",
    "# Convert to years\n",
    "train1.age = (train1.age/365).round(0)\n",
    "\n",
    "# Plot the before\n",
    "plt.figure()\n",
    "train1.age.plot(kind='hist', bins=50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Group age in year bins\n",
    "g_s = 5  # Size of the bins\n",
    "\n",
    "bins = [i for i in range(int(train1.age.min()-train1.age.min()%g_s), int(train1.age.max() + 2*g_s), g_s)]\n",
    "bin_labels = [f'{i}_{i+g_s}' for i in bins[:-1]]\n",
    "\n",
    "train1.age = cut(train1.age, bins=bins, labels=bin_labels, include_lowest=True)\n",
    "\n",
    "# Plot the after\n",
    "plt.figure()\n",
    "ax = train1.age.value_counts().sort_index().plot(kind='bar')\n",
    "plt.title('Age distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arterial pressure features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fnwIf6+Pjg+rVq+Ozzz7D48eP87Tn3gabnZ2d51SQs7Mz3N3dlX2h0+mQlZWl16d+/fpQq9VKH61Wi4oVK2L//v16/ZYvX17ErQW6d+8OMzMzTJ8+Pc/PWgiB+/fv5/vYV93nL67bzs4ONWrU0Hs95AbJF38HXtbzr3EhBJYuXQoLCwt06NABwF+B2czMrEj7tKi1mZmZISAgAP/973/1TlUlJSVh48aNaNWqFbRa7UtuEZk6HoGhMvXbb7/h/PnzyMrKQlJSEnbv3o2YmBh4enpi27ZtBQ5SFRAQAI1Gg7fffhsffPABHj9+jK+++grOzs64c+eO0u+bb77B8uXL8e6776J69ep49OgRvvrqK2i1WuVN2draGt7e3ti8eTNq1aoFR0dH1KtXD/Xq1cP8+fMRFBQEX19fDBkyRLm12N7evtjDm/fv3x/ffvstxo0bh6NHj6J169ZIS0vDzp07MWrUKHTt2rXAx9eqVQtDhgzBsWPH4OLigjVr1iApKSlPaHuehYUF5s6di0GDBqFNmzbo3bu3cht11apVMXbsWKVvUbd1+vTpiIqKQuvWrTFq1ChkZWXhiy++wBtvvFHkayvc3d0xd+5cXLt2DbVq1cLmzZsRHx+PL7/8stCjU2q1GqtXr0ZQUBDeeOMNDBo0CJUqVcKtW7ewZ88eaLVabN++HY8ePULlypXRo0cPNGzYEHZ2dti5cyeOHTuGBQsWAPgrMIeFheG9995DrVq1kJWVhe+++w5mZmYICQlRnnPo0KGYM2cOhg4diiZNmmD//v3KUYWiqF69OmbNmoXJkyfj2rVr6NatG8qVK4erV69i69atGD58OD766KN8H/8q+9zb2xtt27aFj48PHB0dcfz4ceXW8ly5ofGf//wnAgMDYWZmhl69ehV5+55nZWWFqKgoDBgwAM2aNcNvv/2GyMhIfPzxx8qFwPb29njvvffwxRdfQKVSoXr16oiIiDB4LVBxaps1axZiYmLQqlUrjBo1Cubm5li1ahXS09Mxb968l9oekoSxbn+iv5fcWxhzJ41GI1xdXUXHjh3F4sWLhU6ny/MYQ7dRb9u2TTRo0EBYWVmJqlWrirlz5yq3KufeznzixAnRu3dvUaVKFWFpaSmcnZ1Fly5dxPHjx/XWdejQIeHj4yM0Gk2eWzN37twpWrZsKaytrYVWqxVvv/22OHv2rMFtKug2aiH+utX33//+t/Dy8hIWFhbC1dVV9OjRQ++2T0M8PT1FcHCwiI6OFg0aNBCWlpaiTp06eW7/ze/21M2bN4vGjRsLS0tL4ejoKPr27Stu3ryZ53mKsq1CCLFv3z5lf1WrVk2sXLnS4M/IkDZt2og33nhDHD9+XPj6+gorKyvh6ekpli5danBb8rvF+ffffxfdu3cXFSpUEJaWlsLT01P07NlT7Nq1Swjx163Z48ePFw0bNhTlypUTtra2omHDhmL58uXKOq5cuSIGDx4sqlevLqysrISjo6No166d2Llzp95zPXnyRAwZMkTY29uLcuXKiZ49e4rk5OR8b6POvV34RT///LNo1aqVsLW1Fba2tqJOnToiNDRUXLhwodD9VtR9/uJt1LNmzRJNmzYVDg4OwtraWtSpU0d8+umnyu3qQgiRlZUlRo8eLZycnIRKpVLWmXtb8/z58/PUk99t1La2tuLy5csiICBA2NjYCBcXFxEeHi6ys7P1Hn/37l0REhIibGxsRPny5cUHH3wgEhIS8qwzv9qEyHsbtRB//c4HBgYKOzs7YWNjI9q1aycOHTqk1ye/oRzy+/0h06cSglcuEVHpatu2Le7du4eEhARjl0JErwleA0NERETSYYAhIiIi6TDAEBERkXR4DQwRERFJh0dgiIiISDoMMERERCSd13Ygu5ycHNy+fRvlypUr0SGziYiIqPQIIfDo0SO4u7tDrc7/OMtrG2Bu377N78AgIiKS1I0bN1C5cuV821/bAFOuXDkAf+0AfhcGERGRHHQ6HTw8PJT38fy8tgEm97SRVqtlgCEiIpJMYZd/8CJeIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOsUKMCtWrECDBg2UsVV8fX3x22+/Ke3Pnj1DaGgoKlSoADs7O4SEhCApKUlvHdevX0dwcDBsbGzg7OyM8ePHIysrS6/P3r178eabb8LS0hI1atTAunXrXn4LiYiI6LVTrABTuXJlzJkzB3FxcTh+/Djat2+Prl274syZMwCAsWPHYvv27fjxxx+xb98+3L59G927d1cen52djeDgYGRkZODQoUP45ptvsG7dOkydOlXpc/XqVQQHB6Ndu3aIj4/HmDFjMHToUERHR5fQJhMREZHsVEII8SorcHR0xPz589GjRw84OTlh48aN6NGjBwDg/PnzqFu3LmJjY9G8eXP89ttv6NKlC27fvg0XFxcAwMqVKzFx4kTcvXsXGo0GEydORGRkJBISEpTn6NWrF1JSUhAVFVXkunQ6Hezt7ZGamsqReImIiCRR1Pfvl74GJjs7G5s2bUJaWhp8fX0RFxeHzMxM+Pv7K33q1KmDKlWqIDY2FgAQGxuL+vXrK+EFAAIDA6HT6ZSjOLGxsXrryO2Tu478pKenQ6fT6U1ERET0eip2gDl9+jTs7OxgaWmJESNGYOvWrfD29kZiYiI0Gg0cHBz0+ru4uCAxMREAkJiYqBdecttz2wrqo9Pp8PTp03zrmj17Nuzt7ZWJ30RNRET0+ip2gKlduzbi4+Nx5MgRjBw5EgMGDMDZs2dLo7ZimTx5MlJTU5Xpxo0bxi6JiIiISkmxv41ao9GgRo0aAAAfHx8cO3YMixcvxj/+8Q9kZGQgJSVF7yhMUlISXF1dAQCurq44evSo3vpy71J6vs+Ldy4lJSVBq9XC2to637osLS1haWlZ3M0hIiIiCRU7wLwoJycH6enp8PHxgYWFBXbt2oWQkBAAwIULF3D9+nX4+voCAHx9ffHpp58iOTkZzs7OAICYmBhotVp4e3srfX799Ve954iJiVHWQVTWqk6KLLV1X5sTXGrrJiJ6nRUrwEyePBlBQUGoUqUKHj16hI0bN2Lv3r2Ijo6Gvb09hgwZgnHjxsHR0RFarRajR4+Gr68vmjdvDgAICAiAt7c33n//fcybNw+JiYmYMmUKQkNDlaMnI0aMwNKlSzFhwgQMHjwYu3fvxg8//IDIyNJ7EyEiIiK5FCvAJCcno3///rhz5w7s7e3RoEEDREdHo2PHjgCARYsWQa1WIyQkBOnp6QgMDMTy5cuVx5uZmSEiIgIjR46Er68vbG1tMWDAAMyYMUPp4+XlhcjISIwdOxaLFy9G5cqVsXr1agQGBpbQJhMREZHsXnkcGFPFcWCopPAUEhFR2Sn1cWCIiIiIjIUBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSTrECzOzZs/HWW2+hXLlycHZ2Rrdu3XDhwgW9Pm3btoVKpdKbRowYodfn+vXrCA4Oho2NDZydnTF+/HhkZWXp9dm7dy/efPNNWFpaokaNGli3bt3LbSERERG9dooVYPbt24fQ0FAcPnwYMTExyMzMREBAANLS0vT6DRs2DHfu3FGmefPmKW3Z2dkIDg5GRkYGDh06hG+++Qbr1q3D1KlTlT5Xr15FcHAw2rVrh/j4eIwZMwZDhw5FdHT0K24uERERvQ7Mi9M5KipKb37dunVwdnZGXFwc/Pz8lOU2NjZwdXU1uI4dO3bg7Nmz2LlzJ1xcXNCoUSPMnDkTEydOxLRp06DRaLBy5Up4eXlhwYIFAIC6deviwIEDWLRoEQIDA4u7jURERPSaeaVrYFJTUwEAjo6Oess3bNiAihUrol69epg8eTKePHmitMXGxqJ+/fpwcXFRlgUGBkKn0+HMmTNKH39/f711BgYGIjY2Nt9a0tPTodPp9CYiIiJ6PRXrCMzzcnJyMGbMGLRs2RL16tVTlvfp0weenp5wd3fHqVOnMHHiRFy4cAFbtmwBACQmJuqFFwDKfGJiYoF9dDodnj59Cmtr6zz1zJ49G9OnT3/ZzSEiIiKJvHSACQ0NRUJCAg4cOKC3fPjw4cr/69evDzc3N3To0AGXL19G9erVX77SQkyePBnjxo1T5nU6HTw8PErt+YiIiMh4XuoUUlhYGCIiIrBnzx5Urly5wL7NmjUDAFy6dAkA4OrqiqSkJL0+ufO5183k10er1Ro8+gIAlpaW0Gq1ehMRERG9nooVYIQQCAsLw9atW7F79254eXkV+pj4+HgAgJubGwDA19cXp0+fRnJystInJiYGWq0W3t7eSp9du3bprScmJga+vr7FKZeIiIheU8UKMKGhoVi/fj02btyIcuXKITExEYmJiXj69CkA4PLly5g5cybi4uJw7do1bNu2Df3794efnx8aNGgAAAgICIC3tzfef/99nDx5EtHR0ZgyZQpCQ0NhaWkJABgxYgSuXLmCCRMm4Pz581i+fDl++OEHjB07toQ3n4iIiGRUrACzYsUKpKamom3btnBzc1OmzZs3AwA0Gg127tyJgIAA1KlTB//6178QEhKC7du3K+swMzNDREQEzMzM4Ovri379+qF///6YMWOG0sfLywuRkZGIiYlBw4YNsWDBAqxevZq3UBMREREAQCWEEMYuojTodDrY29sjNTWV18PQK6k6KbLU1n1tTnCprZuISEZFff/mdyERERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdIoVYGbPno233noL5cqVg7OzM7p164YLFy7o9Xn27BlCQ0NRoUIF2NnZISQkBElJSXp9rl+/juDgYNjY2MDZ2Rnjx49HVlaWXp+9e/fizTffhKWlJWrUqIF169a93BYSERHRa6dYAWbfvn0IDQ3F4cOHERMTg8zMTAQEBCAtLU3pM3bsWGzfvh0//vgj9u3bh9u3b6N79+5Ke3Z2NoKDg5GRkYFDhw7hm2++wbp16zB16lSlz9WrVxEcHIx27dohPj4eY8aMwdChQxEdHV0Cm0xERESyUwkhxMs++O7du3B2dsa+ffvg5+eH1NRUODk5YePGjejRowcA4Pz586hbty5iY2PRvHlz/Pbbb+jSpQtu374NFxcXAMDKlSsxceJE3L17FxqNBhMnTkRkZCQSEhKU5+rVqxdSUlIQFRVVpNp0Oh3s7e2RmpoKrVb7sptIhKqTIktt3dfmBJfauomIZFTU9+9XugYmNTUVAODo6AgAiIuLQ2ZmJvz9/ZU+derUQZUqVRAbGwsAiI2NRf369ZXwAgCBgYHQ6XQ4c+aM0uf5deT2yV2HIenp6dDpdHoTERERvZ5eOsDk5ORgzJgxaNmyJerVqwcASExMhEajgYODg15fFxcXJCYmKn2eDy+57bltBfXR6XR4+vSpwXpmz54Ne3t7ZfLw8HjZTSMiIiIT99IBJjQ0FAkJCdi0aVNJ1vPSJk+ejNTUVGW6ceOGsUsiIiKiUmL+Mg8KCwtDREQE9u/fj8qVKyvLXV1dkZGRgZSUFL2jMElJSXB1dVX6HD16VG99uXcpPd/nxTuXkpKSoNVqYW1tbbAmS0tLWFpavszmEBERkWSKdQRGCIGwsDBs3boVu3fvhpeXl167j48PLCwssGvXLmXZhQsXcP36dfj6+gIAfH19cfr0aSQnJyt9YmJioNVq4e3trfR5fh25fXLXQURERH9vxToCExoaio0bN+K///0vypUrp1yzYm9vD2tra9jb22PIkCEYN24cHB0dodVqMXr0aPj6+qJ58+YAgICAAHh7e+P999/HvHnzkJiYiClTpiA0NFQ5gjJixAgsXboUEyZMwODBg7F792788MMPiIwsvbtBiIiISB7FOgKzYsUKpKamom3btnBzc1OmzZs3K30WLVqELl26ICQkBH5+fnB1dcWWLVuUdjMzM0RERMDMzAy+vr7o168f+vfvjxkzZih9vLy8EBkZiZiYGDRs2BALFizA6tWrERgYWAKbTERERLJ7pXFgTBnHgaGSwnFgiIjKTpmMA0NERERkDAwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0ih1g9u/fj7fffhvu7u5QqVT45Zdf9NoHDhwIlUqlN3Xq1Emvz4MHD9C3b19otVo4ODhgyJAhePz4sV6fU6dOoXXr1rCysoKHhwfmzZtX/K0jIiKi11KxA0xaWhoaNmyIZcuW5dunU6dOuHPnjjJ9//33eu19+/bFmTNnEBMTg4iICOzfvx/Dhw9X2nU6HQICAuDp6Ym4uDjMnz8f06ZNw5dfflnccomIiOg1ZF7cBwQFBSEoKKjAPpaWlnB1dTXYdu7cOURFReHYsWNo0qQJAOCLL75A586d8dlnn8Hd3R0bNmxARkYG1qxZA41GgzfeeAPx8fFYuHChXtAhIiKiv6dSuQZm7969cHZ2Ru3atTFy5Ejcv39faYuNjYWDg4MSXgDA398farUaR44cUfr4+flBo9EofQIDA3HhwgU8fPjQ4HOmp6dDp9PpTURERPR6KvEA06lTJ3z77bfYtWsX5s6di3379iEoKAjZ2dkAgMTERDg7O+s9xtzcHI6OjkhMTFT6uLi46PXJnc/t86LZs2fD3t5emTw8PEp604iIiMhEFPsUUmF69eql/L9+/fpo0KABqlevjr1796JDhw4l/XSKyZMnY9y4ccq8TqdjiCEiInpNlfpt1NWqVUPFihVx6dIlAICrqyuSk5P1+mRlZeHBgwfKdTOurq5ISkrS65M7n9+1NZaWltBqtXoTERERvZ5KPcDcvHkT9+/fh5ubGwDA19cXKSkpiIuLU/rs3r0bOTk5aNasmdJn//79yMzMVPrExMSgdu3aKF++fGmXTERERCau2AHm8ePHiI+PR3x8PADg6tWriI+Px/Xr1/H48WOMHz8ehw8fxrVr17Br1y507doVNWrUQGBgIACgbt266NSpE4YNG4ajR4/i4MGDCAsLQ69eveDu7g4A6NOnDzQaDYYMGYIzZ85g8+bNWLx4sd4pIiIiIvr7KvY1MMePH0e7du2U+dxQMWDAAKxYsQKnTp3CN998g5SUFLi7uyMgIAAzZ86EpaWl8pgNGzYgLCwMHTp0gFqtRkhICJYsWaK029vbY8eOHQgNDYWPjw8qVqyIqVOn8hZqoiKqOimy1NZ9bU5wqa2biKioih1g2rZtCyFEvu3R0dGFrsPR0REbN24ssE+DBg3wv//9r7jlERER0d8AvwuJiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJp9gBZv/+/Xj77bfh7u4OlUqFX375Ra9dCIGpU6fCzc0N1tbW8Pf3x8WLF/X6PHjwAH379oVWq4WDgwOGDBmCx48f6/U5deoUWrduDSsrK3h4eGDevHnF3zoiIiJ6LRU7wKSlpaFhw4ZYtmyZwfZ58+ZhyZIlWLlyJY4cOQJbW1sEBgbi2bNnSp++ffvizJkziImJQUREBPbv34/hw4cr7TqdDgEBAfD09ERcXBzmz5+PadOm4csvv3yJTSQiIqLXjXlxHxAUFISgoCCDbUIIfP7555gyZQq6du0KAPj222/h4uKCX375Bb169cK5c+cQFRWFY8eOoUmTJgCAL774Ap07d8Znn30Gd3d3bNiwARkZGVizZg00Gg3eeOMNxMfHY+HChXpBh4iIiP6eSvQamKtXryIxMRH+/v7KMnt7ezRr1gyxsbEAgNjYWDg4OCjhBQD8/f2hVqtx5MgRpY+fnx80Go3SJzAwEBcuXMDDhw8NPnd6ejp0Op3eRERERK+nEg0wiYmJAAAXFxe95S4uLkpbYmIinJ2d9drNzc3h6Oio18fQOp5/jhfNnj0b9vb2yuTh4fHqG0REREQm6bW5C2ny5MlITU1Vphs3bhi7JCIiIiolJRpgXF1dAQBJSUl6y5OSkpQ2V1dXJCcn67VnZWXhwYMHen0MreP553iRpaUltFqt3kRERESvpxINMF5eXnB1dcWuXbuUZTqdDkeOHIGvry8AwNfXFykpKYiLi1P67N69Gzk5OWjWrJnSZ//+/cjMzFT6xMTEoHbt2ihfvnxJlkxEREQSKnaAefz4MeLj4xEfHw/grwt34+Pjcf36dahUKowZMwazZs3Ctm3bcPr0afTv3x/u7u7o1q0bAKBu3bro1KkThg0bhqNHj+LgwYMICwtDr1694O7uDgDo06cPNBoNhgwZgjNnzmDz5s1YvHgxxo0bV2IbTkRERPIq9m3Ux48fR7t27ZT53FAxYMAArFu3DhMmTEBaWhqGDx+OlJQUtGrVClFRUbCyslIes2HDBoSFhaFDhw5Qq9UICQnBkiVLlHZ7e3vs2LEDoaGh8PHxQcWKFTF16lTeQk1EREQAAJUQQhi7iNKg0+lgb2+P1NRUXg9Dr6TqpMhSW/e1OcGlsl4ZayYiAor+/v3a3IVEREREfx8MMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQknWJ/FxLRq+AQ90REVBJ4BIaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDolHmCmTZsGlUqlN9WpU0dpf/bsGUJDQ1GhQgXY2dkhJCQESUlJeuu4fv06goODYWNjA2dnZ4wfPx5ZWVklXSoRERFJyrw0VvrGG29g586d//8k5v//NGPHjkVkZCR+/PFH2NvbIywsDN27d8fBgwcBANnZ2QgODoarqysOHTqEO3fuoH///rCwsMB//vOf0iiXiIiIJFMqAcbc3Byurq55lqempuLrr7/Gxo0b0b59ewDA2rVrUbduXRw+fBjNmzfHjh07cPbsWezcuRMuLi5o1KgRZs6ciYkTJ2LatGnQaDSlUTIRERFJpFSugbl48SLc3d1RrVo19O3bF9evXwcAxMXFITMzE/7+/krfOnXqoEqVKoiNjQUAxMbGon79+nBxcVH6BAYGQqfT4cyZM/k+Z3p6OnQ6nd5EREREr6cSDzDNmjXDunXrEBUVhRUrVuDq1ato3bo1Hj16hMTERGg0Gjg4OOg9xsXFBYmJiQCAxMREvfCS257blp/Zs2fD3t5emTw8PEp2w4iIiMhklPgppKCgIOX/DRo0QLNmzeDp6YkffvgB1tbWJf10ismTJ2PcuHHKvE6nY4ghIiJ6TZX6bdQODg6oVasWLl26BFdXV2RkZCAlJUWvT1JSknLNjKura567knLnDV1Xk8vS0hJarVZvIiIiotdTqQeYx48f4/Lly3Bzc4OPjw8sLCywa9cupf3ChQu4fv06fH19AQC+vr44ffo0kpOTlT4xMTHQarXw9vYu7XKJiIhIAiV+Cumjjz7C22+/DU9PT9y+fRvh4eEwMzND7969YW9vjyFDhmDcuHFwdHSEVqvF6NGj4evri+bNmwMAAgIC4O3tjffffx/z5s1DYmIipkyZgtDQUFhaWpZ0uURERCShEg8wN2/eRO/evXH//n04OTmhVatWOHz4MJycnAAAixYtglqtRkhICNLT0xEYGIjly5crjzczM0NERARGjhwJX19f2NraYsCAAZgxY0ZJl0pERESSKvEAs2nTpgLbrayssGzZMixbtizfPp6envj1119LujQiIiJ6TZTKQHZERC+j6qTIUlnvtTnBpbJeIjIefpkjERERSYcBhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLpMMAQERGRdBhgiIiISDoMMERERCQdBhgiIiKSDgMMERERSYcBhoiIiKRjbuwC6OVVnRRZauu+Nie41NZNRET0qngEhoiIiKTDAENERETSYYAhIiIi6TDAEBERkXQYYIiIiEg6DDBEREQkHQYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIukwwBAREZF0GGCIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNIxN3YBREQyqzopslTWe21OcKmsl+h1wSMwREREJB2TDjDLli1D1apVYWVlhWbNmuHo0aPGLomIiIhMgMkGmM2bN2PcuHEIDw/HiRMn0LBhQwQGBiI5OdnYpREREZGRmew1MAsXLsSwYcMwaNAgAMDKlSsRGRmJNWvWYNKkSUaujohIXrxuh14HJhlgMjIyEBcXh8mTJyvL1Go1/P39ERsba/Ax6enpSE9PV+ZTU1MBADqdrkjPWS88+hUqLljC9MBSWW9O+pNSWS9Q9P1WXKxZH2vWV1p1s2Z9MtZMfx+5ryMhRMEdhQm6deuWACAOHTqkt3z8+PGiadOmBh8THh4uAHDixIkTJ06cXoPpxo0bBWYFkzwC8zImT56McePGKfM5OTl48OABKlSoAJVKVWLPo9Pp4OHhgRs3bkCr1ZbYekuTjDUDctbNmssGay4brLlssGZ9Qgg8evQI7u7uBfYzyQBTsWJFmJmZISkpSW95UlISXF1dDT7G0tISlpaWesscHBxKq0RotVppXmi5ZKwZkLNu1lw2WHPZYM1lgzX/P3t7+0L7mORdSBqNBj4+Pti1a5eyLCcnB7t27YKvr68RKyMiIiJTYJJHYABg3LhxGDBgAJo0aYKmTZvi888/R1pamnJXEhEREf19mWyA+cc//oG7d+9i6tSpSExMRKNGjRAVFQUXFxej1mVpaYnw8PA8p6tMmYw1A3LWzZrLBmsuG6y5bLDml6MSorD7lIiIiIhMi0leA0NERERUEAYYIiIikg4DDBEREUmHAYaIiIikwwBDRERE0mGAISIiIumY7Dgwpij3265luldfRqmpqUhMTAQAuLq6FmlIaSqerKwsnDlzRm8/e3t7w8LCwsiVFY6/h0QEMMAUKiYmBosWLUJsbKzyFd9arRa+vr4YN24c/P39jVxhwWQKA6tXr8bChQtx4cIFveW1a9fGv/71LwwZMsRIlRVOlv2ck5ODqVOnYtmyZUhNTdVrs7e3R1hYGKZPnw612rQOzsr+e0il6969e1izZg1iY2P1fg9btGiBgQMHwsnJycgV5sWaX51p/ZUyMd988w06d+4Me3t7LFq0CBEREYiIiMCiRYvg4OCAzp0747vvvjN2mQatXr0a3t7ecHR0hLe3t97/v/76a2OXl8f8+fPx4YcfomvXrti1axcSEhKQkJCAXbt2oVu3bvjwww/x2WefGbvMPGTbz5MmTcKXX36JOXPm4MqVK0hLS0NaWhquXLmCuXPn4ssvv8TkyZONXaYemX8Pz549i1GjRqFx48Zwc3ODm5sbGjdujFGjRuHs2bPGLs8g2Wo+duwYatWqhSVLlsDe3h5+fn7w8/ODvb09lixZgjp16uD48ePGLlMPay4hgvJVs2ZNsXTp0nzbly1bJmrUqFGGFRXNvHnzhI2NjZg0aZLYs2ePOHv2rDh79qzYs2ePmDx5srC1tRXz5883dpl6qlSpIjZv3pxv+6ZNm4SHh0cZVlQ4Gfezi4uLiIqKyrc9KipKODs7l2FFhZP19/DXX38VGo1GNG/eXISHh4vly5eL5cuXi/DwcNGiRQthaWlZ4M/CGGSsuVmzZmL48OEiJycnT1tOTo4YPny4aN68uREqyx9rLhkMMAWwtLQU58+fz7f9/PnzwsrKqgwrKhoZw4CVlZU4e/Zsvu1nzpwR1tbWZVhR4WTczzY2NuLUqVP5tp88eVLY2tqWYUWFk/X3sEGDBuKTTz7Jtz08PFzUr1+/DCsqnIw1W1lZiXPnzuXbfu7cOZN7fbDmksFTSAV44403CjwNsGbNGnh7e5dhRUWTnJyM+vXr59tev3593Lt3rwwrKtxbb72FOXPmICsrK09bdnY25s6di7feessIleVPxv3ctm1bfPTRRwbrunfvHiZOnIi2bduWfWEFkPX38I8//kDfvn3zbe/duzcuXrxYhhUVTsaaXV1dcfTo0Xzbjx49avQvAX4Ray4ZvIi3AAsWLECXLl0QFRUFf39/5YeTlJSEXbt24cqVK4iMjDRylXnlhoGvv/4a5ub6P2JTDQNLly5FYGAgXF1d4efnp7ev9+/fD41Ggx07dhi5Sn0y7ueVK1eic+fOcHNzQ/369fX28+nTp+Ht7Y2IiAgjV6lP1t/DqlWrIjIyErVr1zbYHhkZCU9PzzKuqmAy1vzRRx9h+PDhiIuLQ4cOHfK8Pr766iuTu36ONZcMfht1Ia5du4YVK1bg8OHDeldd+/r6YsSIEahatapxCzTg1KlTCAwMRGZmZoFhoF69ekauVN+jR4+wfv16g/u6T58+0Gq1Rq5Qn6z7OScnB9HR0Qb3c0BAgMndgQTI+Xv4448/ok+fPggKCjIYvKKiorBx40aEhIQYudL/J2PNALB582YsWrQIcXFxyM7OBgCYmZnBx8cH48aNQ8+ePY1cYV6s+dUxwLymZAsDsuJ+poIcOnQIS5YsyXPbqa+vLz788EP4+voaucK8ZKw5V2ZmpnJ6tGLFilKMa8SaXx4DzEvIzMyU4kUmk7i4OPj4+Bi7jL+NK1eu4MCBA7hz5w7UajWqV68Of39/kw5cLw6+5+bmhrp16/J3kV4be/fuRbNmzWBtbW3sUuRQppcMS2bz5s0iPT1dmf/iiy9ElSpVhFqtFhUqVBDTp083YnVFl5mZKXbs2CFWr14tdu7cKbKysoxdUh4qlUpUr15dfPrpp+LWrVvGLqfIsrKyxOXLl0V2drYQQohnz56JzZs3i++//14kJiYaubq8Hj9+LHr06CFUKpVQqVRCrVYLV1dXYWZmJuzs7Aq8XdlYsrOzxb///W/h4OCg1J07OTg4iClTpij7n/6evvrqK9G/f3+xZs0aIcRfdwDWqVNHeHl5ialTpxq5uqKzsLAo8G5MY0pKStKb//3330X//v1FixYtREhIiNizZ0+Z18QAUwC1Wq380NasWSOsrKzE1KlTRWRkpJg1a5awtbUVX331lZGrzCssLExs375dCCHEjRs3RJ06dYSZmZlwcXERZmZmon79+uLmzZtGrlKfSqUSw4YNE87OzsLc3FwEBweLrVu3mmTYynXy5Enh5uYm1Gq1qFevnrh+/bqoV6+esLW1FXZ2dqJ8+fLi6NGjxi5Tz/Dhw0XLli3F6dOnxcWLF0WPHj3EhAkTRFpamvj666+FjY2N2LBhg7HL1DN+/Hjh5OQkVq5cKa5evSqePHkinjx5Iq5evSpWrVolnJ2dxYQJE4xdZrGdPXtWeHl5GbuMPCIjI8WQIUPE+PHj87yZPnjwQLRr185IlRm2aNEiYWtrK7p37y7c3NzErFmzRIUKFcSsWbPE9OnThVarFatWrTJ2mXoaN25scFKpVKJu3brKvCl5/v3w4MGDwsLCQrRp00aMHz9edOzYUZibm4t9+/aVaU0MMAVQqVTKD6xp06Zi3rx5eu3Lly83uReZEH8NVnb69GkhhBA9e/YU/v7+4u7du0IIIe7fvy+6dOkievToYcwS88jd15mZmeKnn34SnTt3VkLXhAkTxIULF4xdYh6BgYGiR48e4vTp0+LDDz8UdevWFe+9957IyMgQmZmZol+/fsLf39/YZeqpWLGiOH78uDL/4MEDYWVlJdLS0oQQQixdulQ0atTIWOUZJOPge0URHx8v1Gq1scvQs2HDBmFmZiaCg4NFq1athJWVlVi/fr3SnpiYaHI116lTRwndJ06cEObm5mL16tVK++rVq4WPj4+xyjPI3NxcdOrUSUybNk2ZwsPDhVqtFqNGjVKWmZLn3w87duwoBg8erNf+4Ycfivbt25dpTQwwBVCpVCI5OVkI8dcf/vj4eL32S5cuiXLlyhmjtAJZWVmJK1euCCGEqFy5sjhy5Ihe++nTp0XFihWNUVq+nv/lyHXz5k0xY8YMUa1aNaFWq0Xr1q2NVJ1h5cuXVz6hPnnyRJiZment64SEBFGhQgVjlWeQg4OD+OOPP5T5jIwMYW5urrzO//jjD5MbQEvGwfeEEGLs2LEFTv369TO5MNCoUSOxePFiZX7z5s3C1tZWCQSmGGCsra3Fn3/+qcxbWlqKhIQEZf7ixYvCwcHBGKXl68CBA6J69epi6tSpeqc/zc3NxZkzZ4xYWf6e/xvt5uYmYmNj9doTEhLK/H3F9O6XNDFRUVHYtm0brKys8OTJE722Z8+eQaVSGamy/NWqVUsZcKhcuXLKl9/levToEXJycoxRWr4M7cdKlSrhk08+weXLl7Fjxw54eHgYobL8CSGU8V9e/Bf46/ZCU9vPb731FhYvXqzML168GE5OTsqXsD1+/Bh2dnbGKs8gGQffA/7at/v27cPvv/9ucDp//ryxS8zj4sWLePvtt5X5nj17Yvv27RgzZgxWrlxpxMryZ2Njg7S0NGXeyckpz2vY0ACZxtSyZUvExcXhjz/+QIsWLXD58mVjl1Qkjx49gk6ng5WVVZ5vgzf0HlnqyjQuSebFCwZnzZql17569WqTPIW0du1aUblyZbFnzx7x7bffirp164qdO3eKW7duid27d4v69euLoUOHGrtMPYaOwJi6Dh06iCFDhoibN2+K6dOnixo1aohBgwYp7aNGjTK5o0ZxcXHC0dFRuLq6iipVqgiNRiO+//57pX3p0qWif//+Rqwwr9xri8zNzUXjxo1Fp06dRKdOnUTjxo2Fubm5aNCggbh+/bqxy8yjVq1a4rvvvsu3/ffffze5oxmGPlkLIcTevXuFnZ2d+Pe//21yNbds2VJs2rQp3/bt27eLevXqlWFFxbNmzRrh6uoqVq1aJSwsLEz6CIxarRZqtVqoVCrx5Zdf6rX/97//LfPvJONt1K8gIiICFhYWCAwMNHYpeSxcuBCffPIJhBDIzs7W+wTyzjvv4LvvvjOpT9r79u1Dy5Yt84xoa8qOHTuGoKAgPHz4EBUqVMCePXswZMgQ/Pnnn1Cr1Xj48CG2b9+ODh06GLtUPXfu3EFERATS09PRvn17kxyG/0UyDr7Xt29fODs7Y9GiRQbbT548icaNG5vUUbpu3bqhYcOGmD59ep62vXv3okuXLnj69KkyiJkpOHjwIGxtbdGoUSOD7cuXL0dOTg7CwsLKtrBiuHjxIvr27Yvjx48jISHBJH8n9+3bpzfv5uaGWrVqKfOLFy9GRkYGxo8fX2Y1McCUoFGjRmHGjBmoWLGisUsBAKSkpCAmJgZXrlxBTk4O3Nzc0LJlS9SsWdPYpb2y4OBgrF69Gm5ubkatIy0tDefPn0ft2rVhZ2eHZ8+eYcOGDXj69Ck6duyY75DssjCV/SyjxMREpKenm9zQ+wXZt28fDh06hMmTJxts37NnD7799lusXbu2jCt7/eXk5ODRo0fQarUmeWmCKWKAKUFarRbx8fGoVq2asUspFlMLXkVRrlw5nDx5Uqp9zf1cOtLS0hAXFwc/Pz9jl0JEZcj0jrtKTNYsuH79+jwX+lLJ434uHZcuXUK7du2MXcbfQlZWFq5fv27sMorl5MmTMDMzM3YZxcKai4YBhqQNXrLhfqbnyfgmdebMGXh5eRm7jGKT8XePNRdOnismiehvydHRscB2U7qgtLhkfJMyNd27dy+wPTU11eSuKWHNJYMBhohMWnp6OkaOHIn69esbbP/zzz8N3jVjbKb4B78wb775ZoHtT58+LaNKim779u3o2LEjXFxcDLabYsBlzSWDAYaITFqjRo3g4eGBAQMGGGw/efKkSQYYU/yDX5izZ8+iV69e+Z4munPnDv74448yrqpgdevWRUhICIYMGWKwPT4+HhEREWVcVcFYc8lggClB/fr1g1arNXYZfwsff/xxoacW6NWZwn4ODg5GSkpKvu2Ojo7o379/2RVURKb4B78w9erVQ7NmzTBy5EiD7fHx8fjqq6/KuKqC+fj44MSJE/nuZ0tLS1SpUqWMqyoYay4ZvI26CI4ePYrY2Ng8A2g1bdrUyJWVjJEjR2LmzJlGv703IyMDv/zyS5593aJFC3Tt2hUajcao9b0q7ue/l0GDBsHGxgbLli0z2H7u3Dl07twZV69eLePK8vfhhx9CpVLh888/N9h++fJlDB06FHv27CnbwgqQnp6O7Oxs2NjYGLuUImPNJYMBpgDJyckICQnBwYMHUaVKFeVQcFJSEq5fv46WLVvi559/hrOzs5ErNUym4HXp0iUEBgbi9u3baNasmd6+PnLkCCpXrozffvsNNWrUMHKleXE/mxZTGXzPFP/gEzBnzhyMGDECDg4Oxi6lyFhzPsr0iwskExISInx9fcX58+fztJ0/f160aNFC9OjRwwiVFSwpKUm0atVKqFQq4enpKZo2bSqaNm0qPD09hUqlEq1atTK57x3y9/cXXbt2FampqXnaUlNTRdeuXUVAQIARKssf97NpsrOzE5cvXzZ2GcU2e/Zs8fDhQ2OXUSwjR44Ud+/eNXYZxVKuXDnpXh+s2TAGmALY2dmJEydO5Nt+/PhxYWdnV4YVFY2Mwcva2lqcPn063/ZTp04Ja2vrMqyocNzPpknWAMM3qbIh4+uDNRvGi3gLYGlpWeDIqY8ePcrzleKmIDo6Gvv37zf4PTy1a9fGkiVL0LZt27IvrAAODg64du0a6tWrZ7D92rVrJnf4lPuZSpKQ8Gy+jDXT64Mj8RbgH//4BwYMGICtW7fqBRmdToetW7di0KBB6N27txErNEzG4DV06FD0798fixYtwqlTp5CUlISkpCScOnUKixYtwsCBAzF8+HBjl6mH+5mIyIhK9fiO5J49eyZGjBghNBqNUKvVwsrKSlhZWQmVSiU0Go0YOXKkePbsmbHLzGPUqFHC09NTbNmyRe9ah9TUVLFlyxZRtWpVERYWZsQKDZszZ45wc3MTKpVKqNVqoVarhUqlEm5ubmLu3LnGLi8P7mfTJOPhdiHkrJs1lw3WbBjvQioCnU6H48ePIykpCQDg4uKCJk2amOyYL+np6RgzZgzWrFmDrKws5bbYjIwMmJubY8iQIVi0aJHJHR3IdfXqVb07ekz1u1e4n02TDN+gbYiMdbPmssGaDeM1MEWg1WrRvn17ZV6j0eDkyZMmG2AsLS2xYsUKzJ07F3FxcXpvUj4+PiZbdy4vLy8p3kyf38/PB1zuZ+MyhcH3yHS1bt0a1tbWxi6jWFizYTwCU4Bx48YZXL548WL069cPFSpUAAAsXLiwLMsqtrS0NPzwww+4dOkS3N3d0atXL6V2U3HixAmUL19eeUP97rvvsHLlSly/fh2enp4ICwtDr169jFylvtGjR6Nnz55o3bq1sUsplqVLl+Lo0aPo3LkzevXqhe+++w6zZ89GTk4OunfvjhkzZsDc3LQ+2/wdBt/r3Lkzvv76a6OPX1McpjI4IwAkJibiyJEjeq+PZs2awdXV1ciV5Y81vxoGmAKo1Wo0bNgwz10Z+/btQ5MmTWBrawuVSoXdu3cbp8B8eHt748CBA3B0dMSNGzfg5+eHhw8folatWrh8+TLMzc1x+PBhk/r03bBhQyxYsAD+/v5YvXo1/vnPf2LYsGGoW7cuLly4gNWrV2Px4sUYPHiwsUtVqNVqqFQqVK9eHUOGDMGAAQNM+g8PAMyaNQvz5s1DQEAADh48iDFjxmD+/PkYO3Ys1Go1Fi1ahJEjR5rUdwvJPvieKf3BLyqZBmdMS0vDBx98gE2bNkGlUilH3x48eAAhBHr37o1Vq1aZ1ICCrLmElOoVNpKbPXu28PLyErt27dJbbm5uLs6cOWOkqgqnUqmUAdT69u0rWrRoIVJSUoQQQjx69Ej4+/uL3r17G7PEPKytrcW1a9eEEEI0btxYfPnll3rtGzZsEN7e3sYoLV8qlUrs3LlTfPjhh6JixYrCwsJCvPPOO2L79u0iOzvb2OUZVL16dfHzzz8LIYSIj48XZmZmYv369Ur7li1bRI0aNYxVnkGyDr73+PFj0bdvX2FmZibMzc2Fs7OzcHZ2Fubm5sLMzEz069dPpKWlGbtMPTIOzjhkyBBRs2ZNERUVJbKyspTlWVlZIjo6WtSqVUsMHTrUiBXmxZpLBgNMIY4ePSpq1aol/vWvf4mMjAwhhFwBplq1amLHjh167QcPHhQeHh7GKC1fFSpUEMePHxdCCOHs7Czi4+P12i9dumRyA6w9v58zMjLE5s2bRWBgoDAzMxPu7u7i448/FhcvXjRylfqsra3Fn3/+qcxbWFiIhIQEZf7atWvCxsbGGKXlS9bB90zxD35hZByc0cHBQRw8eDDf9gMHDggHB4cyrKhwrLlkcByYQrz11luIi4vD3bt30aRJEyQkJEClUhm7rELl1vjs2bM859QrVaqEu3fvGqOsfAUFBWHFihUAgDZt2uCnn37Sa//hhx9M9hQBAFhYWKBnz56IiorClStXMGzYMGzYsMHgIHfG5OrqirNnzwIALl68iOzsbGUeAM6cOWNy3+2VO/hefkx18L2ff/4Z69atQ2BgIMzMzJTlZmZmCAgIwJo1a/K8zo0tOjoay5YtK3BwxqioKCNUlr+cnJwCr4HSaDTIyckpw4oKx5pLSJnGJcl9//33wsXFRajVapM/AlO/fn3RuHFjYWdnJ3766Se99n379olKlSoZqTrDbt26JapWrSr8/PzEuHHjhLW1tWjVqpUYNmyY8PPzExqNRkRGRhq7TD3PH4ExJCcnJ8/RL2ObMmWKcHJyEkOHDhVeXl5i0qRJokqVKmLFihVi5cqVwsPDQ4wdO9bYZer55JNPRPny5cXChQvFyZMnRWJiokhMTBQnT54UCxcuFI6OjiI8PNzYZeah1WrFsWPH8m0/evSo0Gq1ZVhR4SpUqCD27t2bb/uePXtEhQoVyrCiwvXp00c0btzY4Ne+nDhxQvj4+Ii+ffsaobL8seaSwQBTTDdu3BC//PKLePz4sbFLyde0adP0pqioKL32jz76SPTq1ctI1eXv4cOHYuLEicLb21tYWVkJjUYjPD09RZ8+fQp8IzCWqlWrinv37hm7jGLJzs4Wn376qejSpYv4z3/+I3JycsT3338vPDw8RIUKFcTAgQNN8rUt4+B7pvgHvzAyDs744MED0alTJ6FSqYSjo6OoU6eOqFOnjnB0dBRqtVoEBQWZ3JdksuaSwbuQiEgaMg2+9/DhQ/Tp0wfR0dEoX768cmouOTkZKSkpCAwMxMaNG03q9JfMgzOeO3cOhw8fznPnVJ06dYxcWf5krPn8+fMG71AzRs0MMERk8u7cuYMVK1bgwIEDuHPnDtRqNapVq4Zu3bph4MCBeteYmBoZ36R0Op2Ug2DS3wsDDBGZtOPHj8Pf3x81atSAtbU1YmNj0adPH2RkZCA6Ohre3t6IiopCuXLljF0qGdHNmzfh4OAAOzs7veWZmZmIjY2Fn5+fkSoz7P79+zh16hQaNmwIR0dH3Lt3D19//TXS09Px3nvvoW7dusYusUiqVauG6Oho1KxZs8yfmwGGiExaq1at0LFjR4SHhwMA1q9fj6VLl+Lw4cN4+PAh2rdvDz8/PyxevNjIleb1uo0gnJSUhFWrVmHq1KnGLkVx584ddO3aFXFxcVCpVOjTpw+WL1+uBJmkpCS4u7sjOzvbyJX+v6NHjyIgIAA6nQ4ODg6IiYnBe++9B3Nzc+Tk5OD27ds4cOAA3nzzTWOXqliyZInB5ePGjcOECROUgRn/+c9/lllNDDBEZNJsbGyQkJCgfClcTk4OrKyscOPGDbi4uCAmJgYDBw7ErVu3jFypPtlHEDbk5MmTePPNN00qDAwYMAAXLlzA0qVLkZKSgkmTJkGlUmHHjh0oX748kpKS4ObmZlK3JXfs2BFVq1bFwoULsWrVKixevBidOnXCV199BQAYPHgwHj58iK1btxq50v+nVqtRqVKlPF8z8ueff8Ld3R0WFhZQqVS4cuVKmdXEAENEJq1q1arYsGEDWrZsCeCvT9yVKlVCWloarK2tce3aNdStWxdPnz41cqX6OnbsCFtbW3z77bd5rh3R6XTo378/nj59iujoaCNVmNepU6cKbD9//jx69+5tUgGmUqVK2Lp1q/I1B7mnYG7cuIFdu3YhMzPT5I7AODo64uDBg6hbty4yMzNhZWWF2NhYZRtOnDiBd955Bzdv3jRypf9vxIgROHLkCDZu3Kh3esvCwgInT56Et7d3mddkWt/YRkT0gm7dumHEiBGYP38+LC0tMXPmTLRp00b5ptsLFy6gUqVKRq4yr4MHD+Lo0aMGL3zVarWYOXMmmjVrZoTK8teoUSOoVCoY+lybu9zUBvJMTU1F+fLllXlLS0ts2bIF7733Htq1a4f169cbsTrDMjIylNevhYUFbGxs9L4Qs2LFirh//76xyjNo5cqV2Lp1KwIDAzFhwgSEhYUZuyRwJF4iMmmzZs2Ct7c33n77bXTo0AHp6elYs2aN0q5SqTB79mwjVmiYjCMIOzo64quvvsLVq1fzTFeuXEFERISxS8yjWrVqeY4cmZub48cff0S1atXQpUsXI1WWPw8PD71TLZs2bdIbMf3OnTsm8Q3fL3r33XcRGxuLrVu3IigoSLmuy1h4BIaITJqdnR02b96MZ8+eISsrK89dJgEBAUaqrGBDhw5F//798cknn6BDhw5618Ds2rULs2bNwujRo41cpT4fHx/cvn0bnp6eBttTUlIMHp0xpqCgIHz55ZcICQnRW54bYkJCQnDjxg0jVWdYr169kJycrMwHBwfrtW/bts0kv/kb+OuU3c6dOzFnzhw0btzYqK8HXgNDRFRK5s6di8WLFyMxMVE59SKEgKurK8aMGYMJEyYYuUJ9W7duRVpaGvr162ew/eHDh9i2bRsGDBhQxpXlLysrC0+ePMl3jJqsrCzcunUr31Bmip48eQIzMzOTHDDweXFxcThw4AD69++vdxqvrDDAEBGVMplGEH7d3LhxA+Hh4XqnHU0day4aXgNDRFTKvLy84OvrC19fXyW83LhxA4MHDzZyZcUjY80PHjzAN998Y+wyioU1Fw2vgSEiMoLcP/gyfco2xZq3bdtWYHtZjktSVKy5ZDDAEBGVAlP8g18YGWvu1q1bvrd+5zK1W79Zc8ngNTBERKVArVYX6Q++KQ2wJmPNlSpVwvLly9G1a1eD7fHx8fDx8WHNr8gUa+Y1MEREpcDNzQ1btmxBTk6OwenEiRPGLjEPGWv28fFBXFxcvu2FBTJjYM0lgwGGiKgUmOIf/MLIWPP48ePRokWLfNtr1KiBPXv2lGFFhWPNJYOnkIiISsH//vc/pKWloVOnTgbb09LScPz4cbRp06aMK8ufjDXT3xcDDBEREUmHp5CIiIhIOgwwREREJB0GGCIiIpIOAwwRERFJhwGGiIiIpMMAQ0RERNJhgCEiIiLp/B+NEzf9AEjIoQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>35_40</td>\n",
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       "      <td>95.0</td>\n",
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      ],
      "text/plain": [
       "      age  gender  height  weight    ap_hi  ap_lo  cholesterol  gluc  alco  \\\n",
       "14  35_40       2     181    95.0  120_130  80_90            1     1     1   \n",
       "19  55_60       2     162    56.0  110_120  60_70            1     1     0   \n",
       "38  60_65       2     162    72.0  120_130  70_80            1     1     0   \n",
       "58  40_45       2     172    84.0  130_140  80_90            1     1     0   \n",
       "59  55_60       1     164    64.0  170_180  80_90            1     1     0   \n",
       "\n",
       "    active  cardio  \n",
       "14       1       0  \n",
       "19       1       0  \n",
       "38       1       1  \n",
       "58       1       1  \n",
       "59       1       1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g_s = 10  # Size of the bins\n",
    "\n",
    "for feat, title in zip(['ap_lo', 'ap_hi'], ['Diastolic', 'Systolic']):\n",
    "    bins = [i for i in range(int(train1[feat].min()-train1[feat].min()%g_s), int(train1[feat].max() + 2*g_s), g_s)]\n",
    "    bin_labels = [f'{i}_{i+g_s}' for i in bins[:-1]]\n",
    "\n",
    "    train1[feat] = cut(train1[feat], bins=bins, labels=bin_labels, include_lowest=True)\n",
    "\n",
    "    # Plot the after\n",
    "    plt.figure()\n",
    "    train1[feat].value_counts().sort_index().plot(kind='bar', title= title + ' blood pressure distribution')\n",
    "    plt.show()\n",
    "\n",
    "display(train1.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Dimensionality Reduction\n",
    "We will apply dimensionality reduction techniques before fitting the synthesizer in order to circumvent difficulties of generating categorical data. A trivial choice would be directly applying PCA. While this could work, it is known that PCA does not weight categorical features appropriately, we will use Non-Negative Matrix Factorization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 48 columns</p>\n",
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      "text/plain": [
       "     comp_0    comp_1        comp_2        comp_3  comp_4        comp_5  \\\n",
       "0  0.113442  0.000000  1.631866e-01  0.000000e+00     0.0  0.000000e+00   \n",
       "1  0.166758  0.000000  0.000000e+00  1.281923e-01     0.0  3.850436e-07   \n",
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       "3  0.161758  0.198244  0.000000e+00  3.582063e-08     0.0  0.000000e+00   \n",
       "4  0.000066  0.157398  4.391886e-07  4.333607e-07     0.0  0.000000e+00   \n",
       "\n",
       "     comp_6        comp_7        comp_8        comp_9  ...   comp_38  comp_39  \\\n",
       "0  0.000000  0.000000e+00  0.000000e+00  1.707461e-01  ...  0.000000      0.0   \n",
       "1  0.130857  1.808200e-08  0.000000e+00  4.277740e-07  ...  0.106315      0.0   \n",
       "2  0.000000  3.069947e-08  0.000000e+00  2.065234e-07  ...  0.106315      0.0   \n",
       "3  0.000000  0.000000e+00  8.934734e-08  8.747200e-04  ...  0.106315      0.0   \n",
       "4  0.130857  3.531645e-07  2.335135e-01  3.588480e-02  ...  0.106312      0.0   \n",
       "\n",
       "   comp_40  comp_41  comp_42  comp_43  comp_44  comp_45  comp_46  comp_47  \n",
       "0      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "2      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "3      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "4      0.0      0.0      0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2 = train1.copy()\n",
    "\n",
    "# Define a pipeline for the NMF pre-processing\n",
    "num_cols = list(train2.columns[train2.nunique()>=30])\n",
    "cat_cols = [i for i in train2.columns[train2.nunique()<30]]\n",
    "n_comps=48\n",
    "nmf_pipe = Pipeline([('scaler', RegularDataProcessor(num_cols=num_cols, cat_cols=cat_cols)),\n",
    "                     ('NMF', NMF(n_components=n_comps, init='nndsvd', max_iter=2000))])\n",
    "\n",
    "train2 = DataFrame(nmf_pipe.fit_transform(train2), columns = [f'comp_{i}' for i in range(n_comps)])\n",
    "\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we prepared two train datasets. Let's move to defining a synthesizer and we will move to the comparison from there.\n",
    "\n",
    "## 5. Define and fit a synthesizer\n",
    "From the `YData Synthetic` library we will define a Wasserstein GAN with Gradient Penalty, `WGANGP`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define Model Parameters\n",
    "batch_size = 128\n",
    "learning_rate = 5e-4\n",
    "beta_1 = 0.5\n",
    "beta_2 = 0.9\n",
    "noise_dim = 64\n",
    "dim = 128\n",
    "\n",
    "gan_args = ModelParameters(batch_size=batch_size,\n",
    "                           lr=learning_rate,\n",
    "                           betas=(beta_1, beta_2),\n",
    "                           noise_dim=noise_dim,\n",
    "                           layers_dim=dim)\n",
    "\n",
    "# Define Train Parameters\n",
    "log_step = 100\n",
    "epochs = 200\n",
    "\n",
    "train_args = TrainParameters(epochs=epochs,\n",
    "                             sample_interval=log_step)\n",
    "\n",
    "# Instantiate synthesizers\n",
    "synth1 = WGAN_GP(gan_args, n_critic=5)\n",
    "synth2 = WGAN_GP(gan_args, n_critic=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we fit the synthesizers by running the `train` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "num_cols1 = ['height', 'weight']\n",
    "synth1.train(train1, train_args, num_cols = num_cols1, cat_cols = [col for col in train1.columns if col not in num_cols1])\n",
    "synth2.train(train2, train_args, num_cols = [col for col in train2.columns], cat_cols = [])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Sample and post-process synthetic data\n",
    "With the fitted synthesizers we can obtain synthetic samples by leveraging the `sample` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Synthetic data generation: 100%|██████████| 48/48 [00:00<00:00, 113.81it/s]\n",
      "Synthetic data generation: 100%|██████████| 48/48 [00:00<00:00, 508.51it/s]\n"
     ]
    }
   ],
   "source": [
    "synth_data1 = synth1.sample(len(smoker_data)).iloc[:len(smoker_data)]\n",
    "synth_data2 = synth2.sample(len(smoker_data)).iloc[:len(smoker_data)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.1 - Revert dimensionality reduction\n",
    "We undo the changes applied to flow 2 first by inverting the NMF transformation and the pre-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_data2 = nmf_pipe.inverse_transform(synth_data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6.2 - Revert feature engineering\n",
    "Both synthetic samples are in the same format. We need to undo the feature engineering step to put both in the original format.\n",
    "\n",
    "#### Age\n",
    "For age we can sample an integer uniformly within each range, add low scale normal noise and convert years back to days. This should return the spiky behavior we could verify in the real data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for sample in [synth_data1, synth_data2]:\n",
    "    sample['age'] = sample.age.apply(lambda x: x.split('_')[0]).astype('float') + randint(0, 5, len(sample)) + normal(0, 1/6, len(sample))\n",
    "    sample['age'] = (sample.age*365).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Arterial pressure features\n",
    "\n",
    "These features don't need much, in the feature report analysis we got the suspicion that most of it is already rounded to the nearest 10.\n",
    "\n",
    "Let's validate from the whole dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feat, title in zip(['ap_lo', 'ap_hi'], ['diastolic', 'systolic']):\n",
    "    # Plot the after\n",
    "    plt.figure()\n",
    "    (data[feat]%10).value_counts(normalize=True).sort_index().plot(kind='bar', title='Distribution of rest 10 division in '+title+' blood pressure')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More than 95% of the real records are actually rounded to 0. We will use this information to simplify the post-processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14957</td>\n",
       "      <td>1</td>\n",
       "      <td>172</td>\n",
       "      <td>53.0</td>\n",
       "      <td>120</td>\n",
       "      <td>90</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21504</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>58.0</td>\n",
       "      <td>110</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16869</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>62.0</td>\n",
       "      <td>210</td>\n",
       "      <td>60</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19273</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>66.0</td>\n",
       "      <td>210</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15355</td>\n",
       "      <td>1</td>\n",
       "      <td>167</td>\n",
       "      <td>66.0</td>\n",
       "      <td>70</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  gender  height  weight ap_hi ap_lo  cholesterol  gluc  alco  active  \\\n",
       "0  14957       1     172    53.0   120    90            1     2     1       0   \n",
       "1  21504       2     164    58.0   110   120            1     2     0       1   \n",
       "2  16869       2     159    62.0   210    60            2     3     0       0   \n",
       "3  19273       1     156    66.0   210   110            1     1     0       0   \n",
       "4  15355       1     167    66.0    70   100            3     1     0       1   \n",
       "\n",
       "   cardio  \n",
       "0       1  \n",
       "1       1  \n",
       "2       0  \n",
       "3       0  \n",
       "4       1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>ap_hi</th>\n",
       "      <th>ap_lo</th>\n",
       "      <th>cholesterol</th>\n",
       "      <th>gluc</th>\n",
       "      <th>alco</th>\n",
       "      <th>active</th>\n",
       "      <th>cardio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16483</td>\n",
       "      <td>2</td>\n",
       "      <td>169</td>\n",
       "      <td>75.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15315</td>\n",
       "      <td>2</td>\n",
       "      <td>165</td>\n",
       "      <td>64.0</td>\n",
       "      <td>110</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18209</td>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>87.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19331</td>\n",
       "      <td>2</td>\n",
       "      <td>175</td>\n",
       "      <td>87.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20428</td>\n",
       "      <td>2</td>\n",
       "      <td>183</td>\n",
       "      <td>85.0</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  gender  height  weight ap_hi ap_lo  cholesterol  gluc  alco  active  \\\n",
       "0  16483       2     169    75.0   130    80            2     1     0       0   \n",
       "1  15315       2     165    64.0   110    70            1     1     0       1   \n",
       "2  18209       2     164    87.0   130    80            1     1     0       1   \n",
       "3  19331       2     175    87.0   130    80            2     1     0       0   \n",
       "4  20428       2     183    85.0   130    80            1     1     1       1   \n",
       "\n",
       "   cardio  \n",
       "0       1  \n",
       "1       0  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sample in [synth_data1, synth_data2]:\n",
    "    sample['ap_hi'] = sample.ap_hi.apply(lambda x: x.split('_')[0])\n",
    "    sample['ap_lo'] = sample.ap_lo.apply(lambda x: x.split('_')[0])\n",
    "    sample['weight'] = sample['weight'].round(0).round(1)\n",
    "    display(sample.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the synthetic data seems to be in the original data format.\n",
    "\n",
    "The pre and post processing operations are summarized by the following diagram:\n",
    "\n",
    "![title](./img/processingdiagram.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Compare sample profiles\n",
    "To compare the synthetic and real data we start by creating a profile for the synthetic samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_profile1 = Profiler(synth_data1)\n",
    "synth_profile2 = Profiler(synth_data2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `plot_histograms` method to validate the quality of synthetic samples and get a preliminary inspection of the fidelity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "synth_fig1 = graphs.plot_histograms(synth_profile1)\n",
    "synth_fig1.suptitle('Synthetic data sample #1 - Feature Engineering')\n",
    "synth_fig1.tight_layout()\n",
    "\n",
    "synth_fig2 = graphs.plot_histograms(synth_profile2)\n",
    "synth_fig2.suptitle('Synthetic data sample #2 - Feature Engineering + NMF')\n",
    "synth_fig2.tight_layout()\n",
    "\n",
    "real_fig = graphs.plot_histograms(smoker_profile)\n",
    "real_fig.suptitle('Real data sample')\n",
    "real_fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By inspecting the marginal plots, it seems that feature engineering + NMF flow produced better results and was indeed successful in preserving the categorical distributions of the real data while flow 1 is showing a tendency towards uniform distributions. The age values seem to not be capturing the centers of mass of the distribution so well. Probably the strategy of binning by 5, sampling and adding noise took away too much information and the sampling + noise addition strategy was not able to retrieve this behavior. It seems also that glucose feature must be representing only the majority class.\n",
    "\n",
    "The `diff` method of the `Profiler` allows us to obtain a description of the differences between two data profiles, as long as they share the same schema. This allows us to get a glimpse of the similarities between two samples, a useful way to compare synthetic samples with the real data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_diff_report = smoker_profile.diff(synth_profile2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'column_count': 'unchanged',\n",
      " 'correlation_matrix': None,\n",
      " 'duplicate_row_count': 'unchanged',\n",
      " 'encoding': 'unchanged',\n",
      " 'file_type': 'unchanged',\n",
      " 'profile_schema': [{},\n",
      "                    {'active': 'unchanged',\n",
      "                     'age': 'unchanged',\n",
      "                     'alco': 'unchanged',\n",
      "                     'ap_hi': 'unchanged',\n",
      "                     'ap_lo': 'unchanged',\n",
      "                     'cardio': 'unchanged',\n",
      "                     'cholesterol': 'unchanged',\n",
      "                     'gender': 'unchanged',\n",
      "                     'gluc': 'unchanged',\n",
      "                     'height': 'unchanged',\n",
      "                     'weight': 'unchanged'},\n",
      "                    {}],\n",
      " 'row_count': 'unchanged',\n",
      " 'row_has_null_ratio': 'unchanged',\n",
      " 'row_is_null_ratio': 'unchanged',\n",
      " 'samples_used': 'unchanged',\n",
      " 'unique_row_ratio': 0.0013242840589305915}\n"
     ]
    }
   ],
   "source": [
    "pprint({k:v for k,v in synth_diff_report['global_stats'].items() if k!='chi2_matrix'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The general format of the dataset is entirely kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'age': {'categorical': 'unchanged',\n",
      "         'column_name': 'age',\n",
      "         'data_label': 'unchanged',\n",
      "         'data_type': 'unchanged',\n",
      "         'order': 'unchanged',\n",
      "         'statistics': {'max': 335.0,\n",
      "                        'mean': 2490.857399999997,\n",
      "                        'median': 3089.422914285711,\n",
      "                        'median_absolute_deviation': 870.3677818681326,\n",
      "                        'min': 1578.0,\n",
      "                        'mode': [[20422.2375],\n",
      "                                 [],\n",
      "                                 [14995.850999999999, 15708.597000000002]],\n",
      "                        'null_count': 'unchanged',\n",
      "                        'null_types': 'unchanged',\n",
      "                        'null_types_index': 'unchanged',\n",
      "                        'sample_size': 'unchanged',\n",
      "                        'stddev': 769.7272533033104,\n",
      "                        'sum': 12454287.0,\n",
      "                        'unique_count': 885,\n",
      "                        'unique_ratio': 0.17700000000000005,\n",
      "                        'variance': 3240992.0210504895}},\n",
      " 'ap_hi': {'categorical': 'unchanged',\n",
      "           'column_name': 'ap_hi',\n",
      "           'data_label': 'unchanged',\n",
      "           'data_type': 'unchanged',\n",
      "           'order': 'unchanged',\n",
      "           'statistics': {'gini_impurity': 0.2197928800000004,\n",
      "                          'max': 50.0,\n",
      "                          'mean': 8.804000000000002,\n",
      "                          'median': 0.019720467836265243,\n",
      "                          'median_absolute_deviation': 0.015445966555212465,\n",
      "                          'min': 'unchanged',\n",
      "                          'mode': [[119.97], [], [129.995]],\n",
      "                          'null_count': 'unchanged',\n",
      "                          'null_types': 'unchanged',\n",
      "                          'null_types_index': 'unchanged',\n",
      "                          'sample_size': 'unchanged',\n",
      "                          'stddev': 5.994940328001494,\n",
      "                          'sum': 44020.0,\n",
      "                          'unalikeability': 0.21983684736947395,\n",
      "                          'unique_count': 47,\n",
      "                          'unique_ratio': 0.009399999999999999,\n",
      "                          'variance': 173.3822444488898}},\n",
      " 'gluc': {'categorical': 'unchanged',\n",
      "          'column_name': 'gluc',\n",
      "          'data_label': 'unchanged',\n",
      "          'data_type': 'unchanged',\n",
      "          'order': ['random', 'constant value'],\n",
      "          'statistics': {'gini_impurity': 0.26853407999999995,\n",
      "                         'max': 2.0,\n",
      "                         'mean': 0.21760000000000002,\n",
      "                         'median': 0.0011786892975012009,\n",
      "                         'median_absolute_deviation': 'unchanged',\n",
      "                         'min': 'unchanged',\n",
      "                         'mode': [[1.001], [], [1.0]],\n",
      "                         'null_count': 'unchanged',\n",
      "                         'null_types': 'unchanged',\n",
      "                         'null_types_index': 'unchanged',\n",
      "                         'sample_size': 'unchanged',\n",
      "                         'stddev': 0.549827884105952,\n",
      "                         'sum': 1088.0,\n",
      "                         'unalikeability': 0.2685877975595119,\n",
      "                         'unique_count': 2,\n",
      "                         'unique_ratio': 0.00039999999999999996,\n",
      "                         'variance': 0.3023107021404281}}}\n"
     ]
    }
   ],
   "source": [
    "show_feats = ['age', 'ap_hi', 'gluc']\n",
    "pprint(summarize_data_stats(synth_diff_report['data_stats'],show_feats))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This output is very rich, we can unpack the following observations:\n",
    "* The distribution for age is indeed off, we can see that the average and median of the differences is actually significative, close to the equivalent of 6 years\n",
    "* The high blood pressure seems to be actually quite good, the standard deviation of the differences is around 6 in a feature with values ranging 80-220, also the mean and median of differences are close to 0\n",
    "* In glucose we can confirm the suspicion taken from the plots, the synthetic sample is a constant value as the field `order` indicates. Well, choosing one at least it got the majority class right!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Saving/loading a profile for later analysis\n",
    "\n",
    "`DataProfiler` also allows saving and loading profiles. This is another a useful feature for your `YData Synthetic` projects. To save a profile you can use the `Profiler`'s `save` method.\n",
    "\n",
    "As an example, we can save the synthesizer and the synthetic data sample profile that we have just created and worry about sample evaluation or model selection after a batch of experiments.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_profile2.save(filepath='synthetic_data_profile.pkl')\n",
    "synth2.save(path='synthesizer.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reloading data profiles with the `load` method is easy as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "synth_profile = Profiler.load(filepath='synthetic_data_profile.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "`DataProfiler` has a set of tools that are good additions to your Machine Learning flows.\n",
    "\n",
    "We have seen how we can get an overall and more detailed profile of our data via plots or rich dictionaries. We have also learned how to assess the missingness of the dataset.\n",
    "\n",
    "To compare the quality of synthetic samples we used the `diff` method that provided us with a special type of `Profiler` directed at the differences of the two samples.\n",
    "\n",
    "We learned that training the `WGANGP` in the dense representation seemed to yield results with higher fidelity than directly learning in the data space. There are many different strategies that could have been applied to pre-processing, `DataProfiler` was useful in testing the different possibilities.\n",
    "\n",
    "We hope this notebook has demonstrated the capacities of the `DataProfiler` package and how you can use it in the context of a `YData Synthetic` project. "
   ]
  }
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