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import io | |
import gradio as gr | |
import os | |
import tempfile | |
import numpy as np | |
import pandas as pd | |
def greet( | |
file_obj: tempfile._TemporaryFileWrapper, | |
col_to_fit: str, | |
niterations: int, | |
binary_operators: list, | |
unary_operators: list, | |
): | |
empty_df = pd.DataFrame( | |
{ | |
"equation": [], | |
"loss": [], | |
"complexity": [], | |
} | |
) | |
if col_to_fit == "": | |
return ( | |
empty_df, | |
"Please enter a column to predict!", | |
) | |
if len(binary_operators) == 0 and len(unary_operators) == 0: | |
return ( | |
empty_df, | |
"Please select at least one operator!", | |
) | |
if file_obj is None: | |
return ( | |
empty_df, | |
"Please upload a CSV file!", | |
) | |
niterations = int(niterations) | |
# Need to install PySR in separate python instance: | |
os.system( | |
"""if [ ! -d "$HOME/.julia/environments/pysr-0.9.1" ] | |
then | |
python -c 'import pysr; pysr.install()' | |
fi""" | |
) | |
from pysr import PySRRegressor | |
df = pd.read_csv(file_obj.name) | |
y = np.array(df[col_to_fit]) | |
X = df.drop([col_to_fit], axis=1) | |
model = PySRRegressor( | |
update=False, | |
temp_equation_file=True, | |
niterations=niterations, | |
binary_operators=binary_operators, | |
unary_operators=unary_operators, | |
) | |
model.fit(X, y) | |
df = model.equations_[["equation", "loss", "complexity"]] | |
# Convert all columns to string type: | |
df = df.astype(str) | |
return df, "Successful." | |
def main(): | |
demo = gr.Interface( | |
fn=greet, | |
description="A demo of PySR", | |
inputs=[ | |
gr.inputs.File(label="Upload a CSV File"), | |
gr.inputs.Textbox(label="Column to Predict", placeholder="y"), | |
gr.inputs.Slider( | |
minimum=1, | |
maximum=1000, | |
value=40, | |
label="Number of iterations", | |
), | |
gr.inputs.CheckboxGroup( | |
choices=["+", "-", "*", "/", "^"], | |
label="Binary Operators", | |
value=["+", "-", "*", "/"], | |
), | |
gr.inputs.CheckboxGroup( | |
choices=["sin", "cos", "exp", "log"], | |
label="Unary Operators", | |
value=[], | |
), | |
], | |
outputs=[ | |
gr.outputs.DataFrame(label="Equations"), | |
gr.outputs.Textbox(label="Error Log") | |
], | |
) | |
# Add file to the demo: | |
demo.launch() | |
if __name__ == "__main__": | |
main() | |