# Welcome to LINDA
``MELINDA`` is a python library for creating tabular synthetic data.
It uses various generative models in artificial intelligence
to learn statistical properties from your real data and
use them to generate synthetic data.
## Installation
```python
git clone https://github.com/hse-cs/LINDA.git
cd LINDA
pip install -e .
```
or
```python
poetry install
```
## Basic usage
The following code snippet creates an example of real data, fits a generative model, and samples synthetic data.
```python
import numpy as np
import pandas as pd
from melinda.models import ProbaformsSynthesizer
from probaforms.models import CVAE
# generate an example of real data
n = 100
data_real = pd.DataFrame()
data_real['col_1'] = np.random.rand(n)
data_real['col_2'] = np.random.rand(n)
data_real['col_3'] = [str(i) for i in np.random.randint(0, 10, n)]
data_real['col_4'] = [str(i) for i in np.random.randint(0, 5, n)]
num_cols = ['col_1', 'col_2']
cat_cols = ['col_3', 'col_4']
lab_cols = None
# fit a generative model
model = CVAE(latent_dim=10, hidden=(10,), lr=0.001, n_epochs=10)
gen = ProbaformsSynthesizer(model, num_cols, cat_cols, lab_cols, cat_transform='OneHotEncoder')
gen.fit(data_real)
# sample synthetic data
data_synthetic = gen.sample(n_samples=10)
data_synthetic.head()
```
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"description": "# Welcome to LINDA\n\n``MELINDA`` is a python library for creating tabular synthetic data. \nIt uses various generative models in artificial intelligence \nto learn statistical properties from your real data and \nuse them to generate synthetic data.\n\n## Installation\n```python\ngit clone https://github.com/hse-cs/LINDA.git\ncd LINDA\npip install -e .\n```\nor\n```python\npoetry install\n```\n\n## Basic usage\nThe following code snippet creates an example of real data, fits a generative model, and samples synthetic data.\n```python\nimport numpy as np\nimport pandas as pd\nfrom melinda.models import ProbaformsSynthesizer\nfrom probaforms.models import CVAE\n\n# generate an example of real data\nn = 100\ndata_real = pd.DataFrame()\ndata_real['col_1'] = np.random.rand(n)\ndata_real['col_2'] = np.random.rand(n)\ndata_real['col_3'] = [str(i) for i in np.random.randint(0, 10, n)]\ndata_real['col_4'] = [str(i) for i in np.random.randint(0, 5, n)]\n\nnum_cols = ['col_1', 'col_2']\ncat_cols = ['col_3', 'col_4']\nlab_cols = None\n\n# fit a generative model\nmodel = CVAE(latent_dim=10, hidden=(10,), lr=0.001, n_epochs=10)\ngen = ProbaformsSynthesizer(model, num_cols, cat_cols, lab_cols, cat_transform='OneHotEncoder')\ngen.fit(data_real)\n\n# sample synthetic data\ndata_synthetic = gen.sample(n_samples=10)\ndata_synthetic.head()\n```\n",
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