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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=25, # 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]) | |
| examples = gr.Examples(examples=[['diabetes.arff',5], ["titanic.csv", 15]],inputs = [data_input_u,num_samples], outputs = [data_output], cache_examples = True, fn = generate_data) | |
| demo.launch() |