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"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/zredlined/1a9f5ff368dee86007adc1a8362565d0/gretel_synthetics_blog.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "a4H6cFYGF44q",
"colab_type": "text"
},
"source": [
"# Gretel Synthetics Walkthrough\n",
"\n",
"Welcome to the Gretel Synthetics walkthrough! In this tutorial we will take you through the steps of extracting data from Gretel, building a training dataset, creating synthetic data, and validating the new data!\n",
"\n",
"This tutorial assumes you have already created and uploaded data to a [Gretel project](https://console.gretel.cloud).\n",
"\n",
"Let's get started!"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NJ6iSz2QF44r",
"colab_type": "text"
},
"source": [
"## Connect your API key\n",
"\n",
"Input the Gretel API key. Just run the cell below (no need to change it's contents) and then enter your Gretel API KEY in the pop-up box when it appears. This key is used to access premium synthetic data services, and to optionally create a project to stream synthetic data to in the final step."
]
},
{
"cell_type": "code",
"metadata": {
"tags": [
"parameters"
],
"id": "2LdAUOPGF44s",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "e3ca6de9-c967-4f43-d00e-e5c6e8400ab4"
},
"source": [
"import getpass\n",
"import os\n",
"\n",
"gretel_api_key = os.getenv(\"GRETEL_API_KEY\") or getpass.getpass(\"Your Gretel API Key\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Your Gretel API Key··········\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cJ4KVii9ptDq",
"colab_type": "text"
},
"source": [
"## Choose source data\n",
"\n",
"1) Load the source dataset from S3 (or a local disk) into a Pandas Dataframe.\n",
"\n",
"2) Make any changes to the dataframe (e.g. drop or add columns)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PvH_vjmYpgtf",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"dataset_path = 'https://gretel-public-website.s3-us-west-2.amazonaws.com/datasets/healthcare-analytics-vidhya/train_data.csv'\n",
"nrows = 10000\n",
"\n",
"# Load and preview dataset\n",
"training_df = pd.read_csv(dataset_path, nrows=nrows)\n",
"training_df"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "wQl2fjvOF44w",
"colab_type": "text"
},
"source": [
"## Steps to create a synthetic dataset\n",
"\n",
"\n",
"In the code below, we will:\n",
"* Install Gretel packages and dependencies\n",
"* Load source dataset to synthesize\n",
"* Automatically build a record validator from the source data\n",
"* Train a synthetic model (neural network) on the source data\n",
"* Generate `gen_lines` synthetic data records that pass validation\n",
"* Create a synthetic data performance report to compare the source and synthetic datasets"
]
},
{
"cell_type": "code",
"metadata": {
"cell_id": "gretel_synthetics_deps",
"cell_replaced": true,
"id": "x8YyQDycF44w",
"colab_type": "code",
"colab": {}
},
"source": [
"%%capture\n",
"\n",
"!pip install -U gretel-client\n",
"!pip install gretel-synthetics\n",
"\n",
"# NOTE: if you need synthetics AND TensorFlow, use the below\n",
"# !pip install gretel-synthetics[tf]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"cell_id": "gretel_synthetics_boilerplate",
"cell_replaced": true,
"id": "UdfVdf5RF44z",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"outputId": "eeaf8988-e0d8-4f03-de05-d8b3b990ccb1"
},
"source": [
"from gretel_client import get_cloud_client\n",
"\n",
"client = get_cloud_client(\"api\", api_key=gretel_api_key)\n",
"\n",
"client.install_packages()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO pkg_installers.py: Authenticating with package manager\n",
"INFO pkg_installers.py: Installing packages (this might take a while)\n",
"INFO pkg_installers.py: Finished installing Gretel packages\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "m3AGKb2HF448",
"colab_type": "code",
"colab": {}
},
"source": [
"# Create the Gretel Synthtetics Training / Model Configuration\n",
"from pathlib import Path\n",
"\n",
"checkpoint_dir = str(Path.cwd() / \"checkpoints\")\n",
"\n",
"config_template = {\n",
" \"checkpoint_dir\": checkpoint_dir,\n",
" \"dp\": True, # enable differential privacy in training\n",
" \"epochs\": 25, # recommend 15-30 epochs to train production models\n",
" \"gen_lines\": nrows, # number of lines to generate in first batch\n",
" \"vocab_size\": 20000\n",
"}"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "WI32-mpbF447",
"colab_type": "text"
},
"source": [
"## Create a Gretel Synthetic Bundle\n",
"\n",
"Next, we run our bundle automation process. This automates the following actions:\n",
"\n",
"- Automatically detect a field delimiter to be used for the Gretel Synthetics library\n",
"- Automatically detect correlations between columns and create batches of column headers for synthesis\n",
"- Build data validators that ensure generated records are within a range of boundaries learned from your training data\n",
"- Build neural network models\n",
"- Utilize AI models to create synthetic data\n",
"\n",
"\n",
"# Synthetic Configuration\n",
"\n",
"- See [our documentation](https://gretel-synthetics.readthedocs.io/en/stable/api/config.html) for additional config options"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-6T-UV8uF44_",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "be813c53-dd29-4ce1-86aa-a18b1cf81627"
},
"source": [
"from gretel_helpers.synthetics import create_df, SyntheticDataBundle\n",
"\n",
"bundle = SyntheticDataBundle(\n",
" training_df=training_df,\n",
" delimiter=None, # if ``None``, it will try and automatically be detected, otherwise you can set it\n",
" auto_validate=True, # build record validators that learn per-column, these are used to ensure generated records have the same composition as the original\n",
" synthetic_config=config_template, # the config for Synthetics\n",
")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO synthetics.py: Detecting record field delimiter...\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FECGn6CuF45B",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
},
"outputId": "9d7d8bf1-5cac-42a0-d7ee-9440638c7f70"
},
"source": [
"bundle.build()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO synthetics.py: Analyzing DataFrame for optimal column batches and ordering...\n",
"INFO synthetics.py: Creating model and data storage directories...\n",
"INFO batch.py: Creating directory structure for batch jobs...\n",
"INFO synthetics.py: Generating training data from source dataset...\n",
"INFO batch.py: Generating training DF and CSV for batch 0\n",
"INFO synthetics.py: Creating data validators...\n",
"INFO synthetics.py: Creating validator for synthetic batch 0\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ojeDOCCqF45D",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 938
},
"outputId": "9d4a6542-f60c-4222-908a-8c1798fb154b"
},
"source": [
"bundle.train()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"100%|██████████| 10001/10001 [00:00<00:00, 41967.17it/s]\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Epoch 1/25\n",
"100/100 [==============================] - 7s 72ms/step - loss: 4.1065 - accuracy: 0.2036\n",
"Epoch 2/25\n",
"100/100 [==============================] - 7s 72ms/step - loss: 2.1351 - accuracy: 0.5059\n",
"Epoch 3/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.8924 - accuracy: 0.7788\n",
"Epoch 4/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7907 - accuracy: 0.8004\n",
"Epoch 5/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7641 - accuracy: 0.8054\n",
"Epoch 6/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7531 - accuracy: 0.8078\n",
"Epoch 7/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7445 - accuracy: 0.8097\n",
"Epoch 8/25\n",
"100/100 [==============================] - 7s 72ms/step - loss: 0.7375 - accuracy: 0.8114\n",
"Epoch 9/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7316 - accuracy: 0.8126\n",
"Epoch 10/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7278 - accuracy: 0.8138\n",
"Epoch 11/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7225 - accuracy: 0.8150\n",
"Epoch 12/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7187 - accuracy: 0.8157\n",
"Epoch 13/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7143 - accuracy: 0.8170\n",
"Epoch 14/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7129 - accuracy: 0.8172\n",
"Epoch 15/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7111 - accuracy: 0.8180\n",
"Epoch 16/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7089 - accuracy: 0.8187\n",
"Epoch 17/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7073 - accuracy: 0.8190\n",
"Epoch 18/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7050 - accuracy: 0.8191\n",
"Epoch 19/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7047 - accuracy: 0.8196\n",
"Epoch 20/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7034 - accuracy: 0.8197\n",
"Epoch 21/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7013 - accuracy: 0.8200\n",
"Epoch 22/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7016 - accuracy: 0.8200\n",
"Epoch 23/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.7004 - accuracy: 0.8201\n",
"Epoch 24/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.6990 - accuracy: 0.8207\n",
"Epoch 25/25\n",
"100/100 [==============================] - 7s 71ms/step - loss: 0.6990 - accuracy: 0.8204\n",
"DP-SGD with sampling rate = 0.00533% and noise_multiplier = 1.1 iterated over 468681 steps satisfies differential privacy with eps = 0.657 and delta = 8.334569627828128e-07.\n",
"The optimal RDP order is 23.0.\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rAuj1oktF45G",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 115,
"referenced_widgets": [
"1fbc474b78554d2da1da5a3f3d53607e",
"e262341b3d1f4fc1b70547ba942f2b3f",
"f391de35884347f483c92f8abdf09107",
"39d33094d0a145609ba65662fecc9130",
"bc2cb9f9f3724b12abb11e1f509d9cf0",
"105fc45f8d154c9b87cb21a060b14511",
"7bc2f065a79c4d4babb0cb163317d72e",
"71ea132a0a414cbe8321c348c3cb3460",
"d295a76c9a3345778d7201f9c641cd51",
"379f5befd8324a6d94556b34212f1ed7",
"4d53d026de1e43e19be47b9ea455c4dd",
"23dcaa614aa247caa38cc7363e596eba",
"2b0ba719c1ce40af97b5eb11f6479081",
"189839db73fb476db2aa4d9556b71498",
"eefc9b71675b472c9c697eedfb66d9a8",
"1305b43c454c4e7c981fcca708e639a4"
]
},
"outputId": "133d18c8-f6c4-4093-a68e-d62613854479"
},
"source": [
"# optional params:\n",
"#\n",
"# num_lines=500 will override the synthetic config ``num_lines``, set whatever number you need\n",
"# max_invalid=5000 will override the default invalid line limit that terminates execution, set whatever number you need\n",
"bundle.generate(max_invalid=nrows)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "1fbc474b78554d2da1da5a3f3d53607e",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Valid record count ', max=10000.0, style=ProgressStyle(de…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "display_data",
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d295a76c9a3345778d7201f9c641cd51",
"version_minor": 0,
"version_major": 2
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Invalid record count ', max=10000.0, style=ProgressStyle(…"
]
},
"metadata": {
"tags": []
}
},
{
"output_type": "stream",
"text": [
"\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "82lC-zGkr7aW",
"colab_type": "text"
},
"source": [
"# Preview the new synthetic dataframe\n",
"The dataframe below is a synthetic version created from the `gretel-synthetics` model."
]
},
{
"cell_type": "code",
"metadata": {
"id": "srvpTYfXF45I",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 660
},
"outputId": "e0d626c2-9482-4e92-89c0-86a865657746"
},
"source": [
"bundle.get_synthetic_df()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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>case_id</th>\n",
" <th>Hospital_code</th>\n",
" <th>Hospital_type_code</th>\n",
" <th>City_Code_Hospital</th>\n",
" <th>Hospital_region_code</th>\n",
" <th>Available Extra Rooms in Hospital</th>\n",
" <th>Department</th>\n",
" <th>Ward_Type</th>\n",
" <th>Ward_Facility_Code</th>\n",
" <th>Bed Grade</th>\n",
" <th>patientid</th>\n",
" <th>City_Code_Patient</th>\n",
" <th>Type of Admission</th>\n",
" <th>Severity of Illness</th>\n",
" <th>Visitors with Patient</th>\n",
" <th>Age</th>\n",
" <th>Admission_Deposit</th>\n",
" <th>Stay</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>383.0</td>\n",
" <td>17</td>\n",
" <td>e</td>\n",
" <td>1</td>\n",
" <td>X</td>\n",
" <td>4</td>\n",
" <td>anesthesia</td>\n",
" <td>R</td>\n",
" <td>E</td>\n",
" <td>1.0</td>\n",
" <td>120676</td>\n",
" <td>8.0</td>\n",
" <td>Trauma</td>\n",
" <td>Extreme</td>\n",
" <td>3</td>\n",
" <td>51-60</td>\n",
" <td>3899.0</td>\n",
" <td>21-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4687.0</td>\n",
" <td>26</td>\n",
" <td>b</td>\n",
" <td>2</td>\n",
" <td>Y</td>\n",
" <td>4</td>\n",
" <td>gynecology</td>\n",
" <td>Q</td>\n",
" <td>D</td>\n",
" <td>3.0</td>\n",
" <td>43339</td>\n",
" <td>2.0</td>\n",
" <td>Emergency</td>\n",
" <td>Extreme</td>\n",
" <td>2</td>\n",
" <td>71-80</td>\n",
" <td>3921.0</td>\n",
" <td>0-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8145.0</td>\n",
" <td>14</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>X</td>\n",
" <td>2</td>\n",
" <td>anesthesia</td>\n",
" <td>S</td>\n",
" <td>E</td>\n",
" <td>3.0</td>\n",
" <td>64800</td>\n",
" <td>8.0</td>\n",
" <td>Emergency</td>\n",
" <td>Minor</td>\n",
" <td>2</td>\n",
" <td>61-70</td>\n",
" <td>4762.0</td>\n",
" <td>11-20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5595.0</td>\n",
" <td>15</td>\n",
" <td>c</td>\n",
" <td>5</td>\n",
" <td>Z</td>\n",
" <td>8</td>\n",
" <td>anesthesia</td>\n",
" <td>Q</td>\n",
" <td>F</td>\n",
" <td>2.0</td>\n",
" <td>19164</td>\n",
" <td>8.0</td>\n",
" <td>Trauma</td>\n",
" <td>Moderate</td>\n",
" <td>4</td>\n",
" <td>31-40</td>\n",
" <td>6054.0</td>\n",
" <td>21-30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>8697.0</td>\n",
" <td>27</td>\n",
" <td>a</td>\n",
" <td>7</td>\n",
" <td>Y</td>\n",
" <td>3</td>\n",
" <td>gynecology</td>\n",
" <td>R</td>\n",
" <td>C</td>\n",
" <td>3.0</td>\n",
" <td>131225</td>\n",
" <td>8.0</td>\n",
" <td>Emergency</td>\n",
" <td>Moderate</td>\n",
" <td>5</td>\n",
" <td>0-10</td>\n",
" <td>4717.0</td>\n",
" <td>71-80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9995</th>\n",
" <td>9259.0</td>\n",
" <td>15</td>\n",
" <td>c</td>\n",
" <td>5</td>\n",
" <td>Z</td>\n",
" <td>3</td>\n",
" <td>anesthesia</td>\n",
" <td>S</td>\n",
" <td>F</td>\n",
" <td>3.0</td>\n",
" <td>33782</td>\n",
" <td>8.0</td>\n",
" <td>Emergency</td>\n",
" <td>Moderate</td>\n",
" <td>2</td>\n",
" <td>0-10</td>\n",
" <td>4351.0</td>\n",
" <td>0-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9996</th>\n",
" <td>2163.0</td>\n",
" <td>24</td>\n",
" <td>a</td>\n",
" <td>1</td>\n",
" <td>X</td>\n",
" <td>2</td>\n",
" <td>anesthesia</td>\n",
" <td>R</td>\n",
" <td>E</td>\n",
" <td>1.0</td>\n",
" <td>116574</td>\n",
" <td>9.0</td>\n",
" <td>Emergency</td>\n",
" <td>Extreme</td>\n",
" <td>2</td>\n",
" <td>61-70</td>\n",
" <td>5009.0</td>\n",
" <td>0-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9997</th>\n",
" <td>2183.0</td>\n",
" <td>28</td>\n",
" <td>b</td>\n",
" <td>11</td>\n",
" <td>X</td>\n",
" <td>5</td>\n",
" <td>gynecology</td>\n",
" <td>Q</td>\n",
" <td>F</td>\n",
" <td>3.0</td>\n",
" <td>51280</td>\n",
" <td>1.0</td>\n",
" <td>Emergency</td>\n",
" <td>Moderate</td>\n",
" <td>3</td>\n",
" <td>81-90</td>\n",
" <td>3830.0</td>\n",
" <td>0-10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9998</th>\n",
" <td>9821.0</td>\n",
" <td>23</td>\n",
" <td>a</td>\n",
" <td>6</td>\n",
" <td>X</td>\n",
" <td>3</td>\n",
" <td>gynecology</td>\n",
" <td>Q</td>\n",
" <td>F</td>\n",
" <td>1.0</td>\n",
" <td>52663</td>\n",
" <td>8.0</td>\n",
" <td>Emergency</td>\n",
" <td>Extreme</td>\n",
" <td>2</td>\n",
" <td>61-70</td>\n",
" <td>5858.0</td>\n",
" <td>31-40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9999</th>\n",
" <td>5143.0</td>\n",
" <td>23</td>\n",
" <td>a</td>\n",
" <td>6</td>\n",
" <td>X</td>\n",
" <td>4</td>\n",
" <td>gynecology</td>\n",
" <td>R</td>\n",
" <td>F</td>\n",
" <td>3.0</td>\n",
" <td>82254</td>\n",
" <td>1.0</td>\n",
" <td>Trauma</td>\n",
" <td>Moderate</td>\n",
" <td>2</td>\n",
" <td>41-50</td>\n",
" <td>3353.0</td>\n",
" <td>21-30</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10000 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" case_id Hospital_code ... Admission_Deposit Stay\n",
"0 383.0 17 ... 3899.0 21-30\n",
"1 4687.0 26 ... 3921.0 0-10\n",
"2 8145.0 14 ... 4762.0 11-20\n",
"3 5595.0 15 ... 6054.0 21-30\n",
"4 8697.0 27 ... 4717.0 71-80\n",
"... ... ... ... ... ...\n",
"9995 9259.0 15 ... 4351.0 0-10\n",
"9996 2163.0 24 ... 5009.0 0-10\n",
"9997 2183.0 28 ... 3830.0 0-10\n",
"9998 9821.0 23 ... 5858.0 31-40\n",
"9999 5143.0 23 ... 3353.0 21-30\n",
"\n",
"[10000 rows x 18 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DQitjNstF45L",
"colab_type": "text"
},
"source": [
"## View the Performance Report\n",
"\n",
"The Performance Report compares the training data to the newly created synthetic data and assesses their statistical similarity. It shows you both quantitatively and graphically any differences between within field distributions as well as cross field correlations."
]
},
{
"cell_type": "code",
"metadata": {
"id": "qR00eNhRF45L",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "10d5a50f-85d4-41db-8e71-3f47e22fc03e"
},
"source": [
"bundle.generate_report()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"GRETEL SYNTHETICS PERFORMANCE REPORT\n",
"\n",
"DATASET OVERVIEW\n",
"Number of training lines duplicated in synthetic lines: 0\n",
"\n",
"DATASET STATISTICS\n",
"Mean Absolute Error between training and synthetic correlation matrices: 0.0320\n",
"Average field Jenson-Shannon distributional distance: 0.0479\n",
"Note: distance scores range from 0 (no difference) to 1 (maximally different)\n",
"\n",
"FIELD NAME CATEGORY COUNT DISTANCE SCORE \n",
"case_id 23 0.0729 \n",
"Hospital_code 24 0.0789 \n",
"Hospital_type_code 7 0.0444 \n",
"City_Code_Hospital 11 0.0491 \n",
"Hospital_region_code 3 0.0091 \n",
"Available Extra Rooms in Hospital 10 0.0387 \n",
"Department 5 0.0710 \n",
"Ward_Type 5 0.0263 \n",
"Ward_Facility_Code 6 0.0532 \n",
"Bed Grade 4 0.0139 \n",
"patientid 24 0.0284 \n",
"City_Code_Patient 29 0.0648 \n",
"Type of Admission 3 0.0182 \n",
"Severity of Illness 3 0.0131 \n",
"Visitors with Patient 18 0.0530 \n",
"Age 10 0.0596 \n",
"Admission_Deposit 75 0.1039 \n",
"Stay 11 0.0638 \n",
"\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/html": [
"<html>\n",
"<head><meta charset=\"utf-8\" /></head>\n",
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@GAP01
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GAP01 commented Sep 16, 2020 •
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Hi,

Did anyone encounter an error during bundle generation process with worker. Workers are systematically killed :

TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.

But nothing special with memory usage ... Any help would be appreciated.

@johntmyers
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@GAP01 Thanks for reporting this. Is this happening when you try to generate data? We'd be happy to troubleshoot this with you. Feel free to contact us at: customer-support@gretel.ai or hi@gretel.ai and we can look deeper into what is going on.

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