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import os
import pandas as pd
import traceback as tb
import numpy as np
from pysr import PySRRegressor
from argparse import ArgumentParser
# Args:
# niterations
# binary_operators
# unary_operators
# col_to_fit
empty_df = pd.DataFrame(
{
"equation": [],
"loss": [],
"complexity": [],
}
)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--niterations", type=int)
parser.add_argument("--maxsize", type=int)
parser.add_argument("--binary_operators", type=str)
parser.add_argument("--unary_operators", type=str)
parser.add_argument("--col_to_fit", type=str)
parser.add_argument("--filename", type=str)
args = parser.parse_args()
niterations = args.niterations
binary_operators = eval(args.binary_operators)
unary_operators = eval(args.unary_operators)
col_to_fit = args.col_to_fit
filename = args.filename
maxsize = args.maxsize
df = pd.read_csv(filename)
y = np.array(df[col_to_fit])
X = df.drop([col_to_fit], axis=1)
model = PySRRegressor(
progress=False,
verbosity=0,
maxsize=maxsize,
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)
error_message = (
"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)
with open("error.log", "w") as f:
f.write(error_message)
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