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import sys
sys.path.insert(0,'TabPFN') # our submodule of the TabPFN repo (at 045c8400203ebd062346970b4f2c0ccda5a40618)
from scripts.transformer_prediction_interface import TabPFNClassifier
import numpy as np
import pandas as pd
import torch
import gradio as gr
import openml
def compute(table: np.array):
vfunc = np.vectorize(lambda s: len(s))
non_empty_row_mask = (vfunc(table).sum(1) != 0)
table = table[non_empty_row_mask]
empty_mask = table == ''
empty_inds = np.where(empty_mask)
if not len(empty_inds[0]):
return "**Please leave at least one field blank for prediction.**", None
if not np.all(empty_inds[1][0] == empty_inds[1]):
return "**Please only leave fields of one column blank for prediction.**", None
y_column = empty_inds[1][0]
eval_lines = empty_inds[0]
train_table = np.delete(table, eval_lines, axis=0)
eval_table = table[eval_lines]
try:
x_train = torch.tensor(np.delete(train_table, y_column, axis=1).astype(np.float32))
x_eval = torch.tensor(np.delete(eval_table, y_column, axis=1).astype(np.float32))
y_train = train_table[:, y_column]
except ValueError:
return "**Please only add numbers (to the inputs) or leave fields empty.**", None
classifier = TabPFNClassifier(base_path='..', device='cpu')
classifier.fit(x_train, y_train)
y_eval, p_eval = classifier.predict(x_eval, return_winning_probability=True)
# print(file, type(file))
out_table = table.copy().astype(str)
out_table[eval_lines, y_column] = [f"{y_e} (p={p_e:.2f})" for y_e, p_e in zip(y_eval, p_eval)]
return None, out_table
def upload_file(file):
if file.name.endswith('.arff'):
dataset = openml.datasets.OpenMLDataset('t', 'test', data_file=file.name)
X_, _, categorical_indicator_, attribute_names_ = dataset.get_data(
dataset_format="array"
)
df = pd.DataFrame(X_, columns=attribute_names_)
return df
elif file.name.endswith('.csv') or file.name.endswith('.data'):
df = pd.read_csv(file.name, header=None)
df.columns = np.arange(len(df.columns))
print(df)
return df
example = \
[
[1, 2, 1],
[2, 1, 1],
[1, 1, 1],
[2, 2, 2],
[3, 4, 2],
[3, 2, 2],
[2, 3, '']
]
with gr.Blocks() as demo:
gr.Markdown("""This demo allows you to play with the **TabPFN**.
You can either change the table manually (we have filled it with a toy benchmark, sum up to 3 has label 1 and over that label 2).
The network predicts fields you leave empty. Only one column can have empty entries that are predicted.
Please, provide everything but the label column as numeric values. It is ok to encode classes as integers.
""")
inp_table = gr.DataFrame(type='numpy', value=example, headers=[''] * 3)
inp_file = gr.File(
label='Drop either a .csv (without header, only numeric values for all but the labels) or a .arff file.')
examples = gr.Examples(examples=['iris.csv', 'balance-scale.arff'],
inputs=[inp_file],
outputs=[inp_table],
fn=upload_file,
cache_examples=True)
btn = gr.Button("Predict Empty Table Cells")
inp_file.change(fn=upload_file, inputs=inp_file, outputs=inp_table)
out_text = gr.Markdown()
out_table = gr.DataFrame()
btn.click(fn=compute, inputs=inp_table, outputs=[out_text, out_table])
demo.launch() |