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import gradio as gr
import numpy as np
import pandas as pd
import pysr
import tempfile
from typing import Optional
empty_df = pd.DataFrame(
{
"equation": [],
"loss": [],
"complexity": [],
}
)
test_equations = [
"sin(x) + cos(2*x) + tan(x/3)",
]
def generate_data(s: str, num_points: int, noise_level: float):
x = np.linspace(0, 10, num_points)
for (k, v) in {
"sin": "np.sin",
"cos": "np.cos",
"exp": "np.exp",
"log": "np.log",
"tan": "np.tan",
"^": "**",
}.items():
s = s.replace(k, v)
y = eval(s)
noise = np.random.normal(0, noise_level, y.shape)
y_noisy = y + noise
return pd.DataFrame({"x": x}), y_noisy
def greet(
file_obj: Optional[tempfile._TemporaryFileWrapper],
test_equation: str,
num_points: int,
noise_level: float,
niterations: int,
maxsize: int,
binary_operators: list,
unary_operators: list,
force_run: bool,
):
if file_obj is not None:
if len(binary_operators) == 0 and len(unary_operators) == 0:
return (
empty_df,
"Please select at least one operator!",
)
# Look at some statistics of the file:
df = pd.read_csv(file_obj)
if len(df) == 0:
return (
empty_df,
"The file is empty!",
)
if len(df.columns) == 1:
return (
empty_df,
"The file has only one column!",
)
if len(df) > 10_000 and not force_run:
return (
empty_df,
"You have uploaded a file with more than 10,000 rows. "
"This will take very long to run. "
"Please upload a subsample of the data, "
"or check the box 'Ignore Warnings'.",
)
col_to_fit = df.columns[-1]
y = np.array(df[col_to_fit])
X = df.drop([col_to_fit], axis=1)
else:
X, y = generate_data(test_equation, num_points, noise_level)
model = pysr.PySRRegressor(
bumper=True,
maxsize=maxsize,
niterations=niterations,
binary_operators=binary_operators,
unary_operators=unary_operators,
timeout_in_seconds=1000,
)
model.fit(X, y)
df = model.equations_[["equation", "loss", "complexity"]]
# Convert all columns to string type:
df = df.astype(str)
msg = (
"Success!\n"
f"You may run the model locally (faster) with "
f"the following parameters:"
+ f"""
model = PySRRegressor(
niterations={niterations},
binary_operators={str(binary_operators)},
unary_operators={str(unary_operators)},
maxsize={maxsize},
)
model.fit(X, y)"""
)
df.to_csv("pysr_output.csv", index=False)
return df, msg
def main():
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Tab("Example Data"):
# Plot of the example data:
example_plot = gr.ScatterPlot(
x="x",
y="y",
tooltip=["x", "y"],
x_lim=[0, 10],
y_lim=[-5, 5],
width=350,
height=300,
)
test_equation = gr.Radio(
test_equations,
value=test_equations[0],
label="Test Equation"
)
num_points = gr.Slider(
minimum=10,
maximum=1000,
value=100,
label="Number of Data Points",
step=1,
)
noise_level = gr.Slider(
minimum=0, maximum=1, value=0.1, label="Noise Level"
)
with gr.Tab("Upload Data"):
file_input = gr.File(label="Upload a CSV File")
with gr.Row():
binary_operators = gr.CheckboxGroup(
choices=["+", "-", "*", "/", "^"],
label="Binary Operators",
value=["+", "-", "*", "/"],
)
unary_operators = gr.CheckboxGroup(
choices=[
"sin",
"cos",
"exp",
"log",
"square",
"cube",
"sqrt",
"abs",
"tan",
],
label="Unary Operators",
value=[],
)
niterations = gr.Slider(
minimum=1,
maximum=1000,
value=40,
label="Number of Iterations",
step=1,
)
maxsize = gr.Slider(
minimum=7,
maximum=35,
value=20,
label="Maximum Complexity",
step=1,
)
force_run = gr.Checkbox(
value=False,
label="Ignore Warnings",
)
with gr.Column():
with gr.Row():
df = gr.Dataframe(
headers=["Equation", "Loss", "Complexity"],
datatype=["str", "number", "number"],
)
error_log = gr.Textbox(label="Error Log")
with gr.Row():
run_button = gr.Button()
run_button.click(
greet,
inputs=[
file_input,
test_equation,
num_points,
noise_level,
niterations,
maxsize,
binary_operators,
unary_operators,
force_run,
],
outputs=[df, error_log],
)
# Any update to the equation choice will trigger a replot:
for eqn_component in [test_equation, num_points, noise_level]:
eqn_component.change(replot, [test_equation, num_points, noise_level], example_plot)
demo.launch()
def replot(test_equation, num_points, noise_level):
X, y = generate_data(test_equation, num_points, noise_level)
df = pd.DataFrame({"x": X["x"], "y": y})
return df
if __name__ == "__main__":
main()
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