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feat: add new gmm based synth for fast synthesis (#269)
* feat: Add new GMM model for fast synthesis * feat: add save and load for new model * fix: synthesis base class * fix: linter * fix: linter warnings
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examples/regular/models/Fast_Adult_Census_Income_Data.ipynb
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| { | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0, | ||
| "metadata": { | ||
| "colab": { | ||
| "provenance": [] | ||
| }, | ||
| "kernelspec": { | ||
| "name": "python3", | ||
| "display_name": "Python 3" | ||
| }, | ||
| "language_info": { | ||
| "name": "python" | ||
| }, | ||
| "accelerator": "GPU", | ||
| "gpuClass": "standard" | ||
| }, | ||
| "cells": [ | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "#Uncomment to install ydata-synthetic lib\n", | ||
| "#!pip install ydata-synthetic" | ||
| ], | ||
| "metadata": { | ||
| "id": "fwXSWiYu_tl0", | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "# Tabular Synthetic Data Generation with Gaussian Mixture\n", | ||
| "- This notebook is an example of how to use a synthetic data generation methods based on [GMM](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html) to generate synthetic tabular data with numeric and categorical features.\n", | ||
| "\n", | ||
| "## Dataset\n", | ||
| "- The data used is the [Adult Census Income](https://www.kaggle.com/datasets/uciml/adult-census-income) which we will fecth by importing the `pmlb` library (a wrapper for the Penn Machine Learning Benchmark data repository).\n" | ||
| ], | ||
| "metadata": { | ||
| "id": "6T8gjToi_yKA", | ||
| "pycharm": { | ||
| "name": "#%% md\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "from pmlb import fetch_data\n", | ||
| "\n", | ||
| "from ydata_synthetic.synthesizers.regular import RegularSynthesizer\n", | ||
| "from ydata_synthetic.synthesizers import ModelParameters, TrainParameters" | ||
| ], | ||
| "metadata": { | ||
| "id": "Ix4gZ9iSCVZI", | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "## Load the data" | ||
| ], | ||
| "metadata": { | ||
| "id": "I0qyPwoECZ5x", | ||
| "pycharm": { | ||
| "name": "#%% md\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "# Load data\n", | ||
| "data = fetch_data('adult')\n", | ||
| "num_cols = ['age', 'fnlwgt', 'capital-gain', 'capital-loss', 'hours-per-week']\n", | ||
| "cat_cols = ['workclass','education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex',\n", | ||
| " 'native-country', 'target']" | ||
| ], | ||
| "metadata": { | ||
| "id": "YeFPnJVOMVqd", | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| }, | ||
| "execution_count": 2, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "## Create and Train the synthetic data generator" | ||
| ], | ||
| "metadata": { | ||
| "id": "68MoepO0Cpx6", | ||
| "pycharm": { | ||
| "name": "#%% md\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "synth = RegularSynthesizer(modelname='fast')\n", | ||
| "synth.fit(data=data, num_cols=num_cols, cat_cols=cat_cols)" | ||
| ], | ||
| "metadata": { | ||
| "id": "oIHMVgSZMg8_", | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| }, | ||
| "execution_count": null, | ||
| "outputs": [] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "source": [ | ||
| "## Generate new synthetic data" | ||
| ], | ||
| "metadata": { | ||
| "id": "xHK-SRPyDUin", | ||
| "pycharm": { | ||
| "name": "#%% md\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "source": [ | ||
| "synth_data = synth.sample(1000)\n", | ||
| "print(synth_data)" | ||
| ], | ||
| "metadata": { | ||
| "id": "0aa2g0RLMkqe", | ||
| "colab": { | ||
| "base_uri": "https://localhost:8080/" | ||
| }, | ||
| "outputId": "01808aa4-a700-4385-e7df-b2f7abd162a0", | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| }, | ||
| "execution_count": 8, | ||
| "outputs": [ | ||
| { | ||
| "output_type": "stream", | ||
| "name": "stdout", | ||
| "text": [ | ||
| " age workclass fnlwgt education education-num \\\n", | ||
| "0 38.753654 4 179993.565472 8 10.0 \n", | ||
| "1 36.408844 4 245841.807958 9 10.0 \n", | ||
| "2 56.251066 4 400895.076058 11 13.0 \n", | ||
| "3 26.846605 4 240156.201048 11 10.0 \n", | ||
| "4 29.083102 1 5601.059126 11 9.0 \n", | ||
| ".. ... ... ... ... ... \n", | ||
| "995 79.281276 4 30664.183560 1 10.0 \n", | ||
| "996 51.423132 4 414524.980527 1 10.0 \n", | ||
| "997 17.342915 6 177716.451926 11 13.0 \n", | ||
| "998 39.298867 4 132011.369567 15 12.0 \n", | ||
| "999 46.977763 2 92662.371635 9 13.0 \n", | ||
| "\n", | ||
| " marital-status occupation relationship race sex capital-gain \\\n", | ||
| "0 4 0 3 4 0 55.771499 \n", | ||
| "1 6 7 0 4 1 124.337939 \n", | ||
| "2 4 3 3 4 1 27.968087 \n", | ||
| "3 4 6 1 4 0 25.065678 \n", | ||
| "4 6 3 0 4 0 126.269337 \n", | ||
| ".. ... ... ... ... ... ... \n", | ||
| "995 2 0 3 4 1 4.393001 \n", | ||
| "996 4 7 3 2 0 54.841598 \n", | ||
| "997 4 4 4 4 0 99.394428 \n", | ||
| "998 4 14 1 4 1 97.834797 \n", | ||
| "999 4 8 1 4 0 51.258308 \n", | ||
| "\n", | ||
| " capital-loss hours-per-week native-country target \n", | ||
| "0 -1.271118 39.749641 39 1 \n", | ||
| "1 -2.114950 44.488198 39 1 \n", | ||
| "2 1.541738 40.042696 39 1 \n", | ||
| "3 1.148560 39.952615 39 1 \n", | ||
| "4 -1.786768 39.808085 39 0 \n", | ||
| ".. ... ... ... ... \n", | ||
| "995 0.224015 50.580637 39 1 \n", | ||
| "996 1.319341 4.441194 39 1 \n", | ||
| "997 -5.231663 39.779674 39 1 \n", | ||
| "998 1.595817 39.731359 13 1 \n", | ||
| "999 1.129814 39.838415 39 1 \n", | ||
| "\n", | ||
| "[1000 rows x 15 columns]\n" | ||
| ] | ||
| } | ||
| ] | ||
| } | ||
| ] | ||
| } |
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| from ydata_synthetic.preprocessing.regular.processor import RegularDataProcessor | ||
| from ydata_synthetic.preprocessing.timeseries.timeseries_processor import TimeSeriesDataProcessor | ||
|
|
||
| __all__ = [ | ||
| "RegularDataProcessor", | ||
| "TimeSeriesDataProcessor" | ||
| ] |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1 +1,6 @@ | ||
| from ydata_synthetic.synthesizers.gan import ModelParameters, TrainParameters | ||
| from ydata_synthetic.synthesizers.base import ModelParameters, TrainParameters | ||
|
|
||
| __all__ = [ | ||
| "ModelParameters", | ||
| "TrainParameters" | ||
| ] |
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