Duplicate from RamAnanth1/REaLTabFormer

Co-authored-by: Ram Ananth <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+title: REaLTabFormer
+emoji: 🌖
+colorFrom: red
+colorTo: purple
+sdk: gradio
+sdk_version: 3.18.0
+app_file: app.py
+pinned: false
+tags:
+- making-demos
+duplicated_from: RamAnanth1/REaLTabFormer
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,113 @@
+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()

diabetes.arff

@@ -0,0 +1,863 @@
+% 1. Title: Pima Indians Diabetes Database
+% 
+% 2. Sources:
+%    (a) Original owners: National Institute of Diabetes and Digestive and
+%                         Kidney Diseases
+%    (b) Donor of database: Vincent Sigillito ([email protected])
+%                           Research Center, RMI Group Leader
+%                           Applied Physics Laboratory
+%                           The Johns Hopkins University
+%                           Johns Hopkins Road
+%                           Laurel, MD 20707
+%                           (301) 953-6231
+%    (c) Date received: 9 May 1990
+% 
+% 3. Past Usage:
+%     1. Smith,~J.~W., Everhart,~J.~E., Dickson,~W.~C., Knowler,~W.~C., \&
+%        Johannes,~R.~S. (1988). Using the ADAP learning algorithm to forecast
+%        the onset of diabetes mellitus.  In {\it Proceedings of the Symposium
+%        on Computer Applications and Medical Care} (pp. 261--265).  IEEE
+%        Computer Society Press.
+% 
+%        The diagnostic, binary-valued variable investigated is whether the
+%        patient shows signs of diabetes according to World Health Organization
+%        criteria (i.e., if the 2 hour post-load plasma glucose was at least 
+%        200 mg/dl at any survey  examination or if found during routine medical
+%        care).   The population lives near Phoenix, Arizona, USA.
+% 
+%        Results: Their ADAP algorithm makes a real-valued prediction between
+%        0 and 1.  This was transformed into a binary decision using a cutoff of 
+%        0.448.  Using 576 training instances, the sensitivity and specificity
+%        of their algorithm was 76% on the remaining 192 instances.
+% 
+% 4. Relevant Information:
+%       Several constraints were placed on the selection of these instances from
+%       a larger database.  In particular, all patients here are females at
+%       least 21 years old of Pima Indian heritage.  ADAP is an adaptive learning
+%       routine that generates and executes digital analogs of perceptron-like
+%       devices.  It is a unique algorithm; see the paper for details.
+% 
+% 5. Number of Instances: 768
+% 
+% 6. Number of Attributes: 8 plus class 
+% 
+% 7. For Each Attribute: (all numeric-valued)
+%    1. Number of times pregnant
+%    2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
+%    3. Diastolic blood pressure (mm Hg)
+%    4. Triceps skin fold thickness (mm)
+%    5. 2-Hour serum insulin (mu U/ml)
+%    6. Body mass index (weight in kg/(height in m)^2)
+%    7. Diabetes pedigree function
+%    8. Age (years)
+%    9. Class variable (0 or 1)
+% 
+% 8. Missing Attribute Values: None
+% 
+% 9. Class Distribution: (class value 1 is interpreted as "tested positive for
+%    diabetes")
+% 
+%    Class Value  Number of instances
+%    0            500
+%    1            268
+% 
+% 10. Brief statistical analysis:
+% 
+%     Attribute number:    Mean:   Standard Deviation:
+%     1.                     3.8     3.4
+%     2.                   120.9    32.0
+%     3.                    69.1    19.4
+%     4.                    20.5    16.0
+%     5.                    79.8   115.2
+%     6.                    32.0     7.9
+%     7.                     0.5     0.3
+%     8.                    33.2    11.8
+% 
+% 
+%
+%
+%
+%
+% Relabeled values in attribute 'class'
+%    From: 0                       To: tested_negative     
+%    From: 1                       To: tested_positive     
+%
+@relation pima_diabetes
+@attribute 'preg' real
+@attribute 'plas' real
+@attribute 'pres' real
+@attribute 'skin' real
+@attribute 'insu' real
+@attribute 'mass' real
+@attribute 'pedi' real
+@attribute 'age' real
+@attribute 'class' { tested_negative, tested_positive}
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requirements.txt

@@ -0,0 +1,2 @@
+realtabformer
+scipy

titanic.csv

@@ -0,0 +1,892 @@
+PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
+1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
+17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
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