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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()
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