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import gradio as gr
import pandas as pd
from realtabformer import REaLTabFormer
from scipy.io import arff
import os
rtf_model = REaLTabFormer(
model_type="tabular",
epochs=5, # Default is 200
gradient_accumulation_steps=4)
def generate_data(file, num_samples):
if '.arff' in file.name:
data = arff.loadarff(open(file.name,'rt'))
df = pd.DataFrame(data[0])
elif '.csv' in file.name:
df = pd.read_csv(file.name)
rtf_model.fit(df, num_bootstrap=10) # Default is 500
# Generate synthetic data
samples = rtf_model.sample(n_samples=num_samples)
return samples
def generate_relational_data(parent_file, child_file, join_on):
parent_df = pd.read_csv(parent_file.name)
child_df = pd.read_csv(child_file.name)
#Make sure join_on column exists in both
assert ((join_on in parent_df.columns) and
(join_on in child_df.columns))
rtf_model.fit(parent_df.drop(join_on, axis=1), num_bootstrap=100)
pdir = Path("rtf_parent/")
rtf_model.save(pdir)
# # Get the most recently saved parent model,
# # or a specify some other saved model.
# parent_model_path = pdir / "idXXX"
parent_model_path = sorted([
p for p in pdir.glob("id*") if p.is_dir()],
key=os.path.getmtime)[-1]
child_model = REaLTabFormer(
model_type="relational",
parent_realtabformer_path=parent_model_path,
epochs = 25,
output_max_length=None,
train_size=0.8)
child_model.fit(
df=child_df,
in_df=parent_df,
join_on=join_on,
num_bootstrap=10)
# Generate parent samples.
parent_samples = rtf_model.sample(5)
# Create the unique ids based on the index.
parent_samples.index.name = join_on
parent_samples = parent_samples.reset_index()
# Generate the relational observations.
child_samples = child_model.sample(
input_unique_ids=parent_samples[join_on],
input_df=parent_samples.drop(join_on, axis=1),
gen_batch=5)
return parent_samples, child_samples, gr.update(visible = True)
with gr.Blocks() as demo:
gr.Markdown("""
## REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers
""")
gr.HTML('''
<p style="margin-bottom: 10px; font-size: 94%">
This is an unofficial demo for REaLTabFormer, an approach that can be used to generate synthetic data from single tabular data using GPT. The demo is based on the <a href='https://github.com/avsolatorio/REaLTabFormer' style='text-decoration: underline;' target='_blank'> Github </a> implementation provided by the authors.
</p>
''')
gr.HTML('''
<p align="center"><img src="https://github.com/avsolatorio/RealTabFormer/raw/main/img/REalTabFormer_Final_EQ.png" style="width:40%"/></p>
''')
with gr.Column():
with gr.Tab("Upload Data as File: Tabular Data"):
data_input_u = gr.File(label = 'Upload Data File (Currently supports CSV and ARFF)', file_types=[".csv", ".arff"])
num_samples = gr.Slider(label="Number of Samples", minimum=5, maximum=100, value=5, step=10)
generate_data_btn = gr.Button('Generate Synthetic Data')
with gr.Tab("Upload Data as File: Relational Data"):
data_input_parent = gr.File(label = 'Upload Data File for Parent Dataset', file_types=[ ".csv"])
data_input_child = gr.File(label = 'Upload Data File for Child Dataset', file_types=[ ".csv"])
join_on = gr.Textbox(label = 'Column name to join on')
generate_data_btn_relational = gr.Button('Generate Synthetic Data')
with gr.Row():
#data_sample = gr.Dataframe(label = "Original Data")
data_output = gr.Dataframe(label = "Synthetic Data")
with gr.Row(visible = False) as child_sample:
data_output_child = gr.Dataframe(label = "Synthetic Data for Child Dataset")
generate_data_btn.click(generate_data, inputs = [data_input_u,num_samples], outputs = [data_output])
generate_data_btn_relational.click(generate_relational_data, inputs = [data_input_parent,data_input_child,join_on], outputs = [data_output, data_output_child, child_sample])
demo.launch() |