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import multiprocessing as mp
import os
import tempfile
import time
from pathlib import Path
from typing import Callable
import numpy as np
import pandas as pd
from data import generate_data, read_csv
from plots import plot_predictions
def empty_df():
return pd.DataFrame(
{
"Equation": [],
"Loss": [],
"Complexity": [],
}
)
def pysr_fit(queue: mp.Queue, out_queue: mp.Queue):
import pysr
while True:
# Get the arguments from the queue, if available
args = queue.get()
if args is None:
break
X = args["X"]
y = args["y"]
kwargs = args["kwargs"]
model = pysr.PySRRegressor(
progress=False,
timeout_in_seconds=1000,
**kwargs,
)
model.fit(X, y)
out_queue.put(None)
def pysr_predict(queue: mp.Queue, out_queue: mp.Queue):
while True:
args = queue.get()
if args is None:
break
X = args["X"]
equation_file = str(args["equation_file"])
index = args["index"]
equation_file_pkl = equation_file.replace(".csv", ".pkl")
equation_file_bkup = equation_file + ".bkup"
equation_file_copy = equation_file.replace(".csv", "_copy.csv")
equation_file_pkl_copy = equation_file.replace(".csv", "_copy.pkl")
# TODO: See if there is way to get lock on file
os.system(f"cp {equation_file_bkup} {equation_file_copy}")
os.system(f"cp {equation_file_pkl} {equation_file_pkl_copy}")
# Note that we import pysr late in this process to avoid
# pre-compiling the code in two places at once
import pysr
try:
model = pysr.PySRRegressor.from_file(equation_file_pkl_copy, verbosity=0)
except pd.errors.EmptyDataError:
continue
ypred = model.predict(X, index)
# Rename the columns to uppercase
equations = model.equations_[["complexity", "loss", "equation"]].copy()
# Remove any row that has worse loss than previous row:
equations = equations[equations["loss"].cummin() == equations["loss"]]
# TODO: Why is this needed? Are rows not being removed?
equations.columns = ["Complexity", "Loss", "Equation"]
out_queue.put(dict(ypred=ypred, equations=equations))
class ProcessWrapper:
def __init__(self, target: Callable[[mp.Queue, mp.Queue], None]):
self.queue = mp.Queue(maxsize=1)
self.out_queue = mp.Queue(maxsize=1)
self.process = mp.Process(target=target, args=(self.queue, self.out_queue))
self.process.start()
ACTIVE_PROCESS = None
def _random_string():
return "".join(list(np.random.choice("abcdefghijklmnopqrstuvwxyz".split(), 16)))
def processing(
*,
file_input,
force_run,
test_equation,
num_points,
noise_level,
data_seed,
niterations,
maxsize,
binary_operators,
unary_operators,
plot_update_delay,
parsimony,
populations,
population_size,
ncycles_per_iteration,
elementwise_loss,
adaptive_parsimony_scaling,
optimizer_algorithm,
optimizer_iterations,
batching,
batch_size,
**kwargs,
):
# random string:
global ACTIVE_PROCESS
cur_process = _random_string()
ACTIVE_PROCESS = cur_process
"""Load data, then spawn a process to run the greet function."""
print("Starting PySR fit process")
writer = ProcessWrapper(pysr_fit)
print("Starting PySR predict process")
reader = ProcessWrapper(pysr_predict)
if file_input is not None:
try:
X, y = read_csv(file_input, force_run)
except ValueError as e:
return (empty_df(), plot_predictions([], []), str(e))
else:
X, y = generate_data(test_equation, num_points, noise_level, data_seed)
tmpdirname = tempfile.mkdtemp()
base = Path(tmpdirname)
equation_file = base / "hall_of_fame.csv"
# Check if queue is empty, if not, kill the process
# and start a new one
if not writer.queue.empty():
print("Restarting PySR fit process")
if writer.process.is_alive():
writer.process.terminate()
writer.process.join()
writer = ProcessWrapper(pysr_fit)
if not reader.queue.empty():
print("Restarting PySR predict process")
if reader.process.is_alive():
reader.process.terminate()
reader.process.join()
reader = ProcessWrapper(pysr_predict)
writer.queue.put(
dict(
X=X,
y=y,
kwargs=dict(
niterations=niterations,
maxsize=maxsize,
binary_operators=binary_operators,
unary_operators=unary_operators,
equation_file=equation_file,
parsimony=parsimony,
populations=populations,
population_size=population_size,
ncycles_per_iteration=ncycles_per_iteration,
elementwise_loss=elementwise_loss,
adaptive_parsimony_scaling=adaptive_parsimony_scaling,
optimizer_algorithm=optimizer_algorithm,
optimizer_iterations=optimizer_iterations,
batching=batching,
batch_size=batch_size,
),
)
)
last_yield = (
pd.DataFrame({"Complexity": [], "Loss": [], "Equation": []}),
plot_predictions([], []),
"Started!",
)
yield last_yield
while writer.out_queue.empty():
if (
equation_file.exists()
and Path(str(equation_file).replace(".csv", ".pkl")).exists()
):
# First, copy the file to a the copy file
reader.queue.put(
dict(
X=X,
equation_file=equation_file,
index=-1,
)
)
out = reader.out_queue.get()
predictions = out["ypred"]
equations = out["equations"]
last_yield = (
equations[["Complexity", "Loss", "Equation"]],
plot_predictions(y, predictions),
"Running...",
)
yield last_yield
if cur_process != ACTIVE_PROCESS:
# Kill both reader and writer
writer.process.kill()
reader.process.kill()
yield (*last_yield[:-1], "Stopped.")
return
time.sleep(0.1)
yield (*last_yield[:-1], "Done.")
return
def stop():
global ACTIVE_PROCESS
ACTIVE_PROCESS = None
return
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