Getting a uft-8 Error

[AaronRoseDA]

Hi Miles,

I'm really struggling to deal with a particular utf-8 encoding issue. I have no problems with any other dataset so I think it might be the data frame but I can't find anything that stands out in the data:

C:\Users\arose\.conda\envs\test-env\Lib\site-packages\pysr\sr.py:2774: UserWarning: Note: it looks like you are running in Jupyter. The progress bar will be turned off.
  warnings.warn(
[ Info: Started!
[ Info: Final population:
[ Info: Results saved to:
Elapsed time: 64.96 seconds
Error in callback _flush_stdio (for post_execute), with arguments args (),kwargs {}:
---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
File ~\.julia\packages\PythonCall\WMWY0\src\JlWrap\any.jl:262, in __call__(self, *args, **kwargs)
    260     return ValueBase.__dir__(self) + self._jl_callmethod($(pyjl_methodnum(pyjlany_dir)))
    261 def __call__(self, *args, **kwargs):
--> 262     return self._jl_callmethod($(pyjl_methodnum(pyjlany_call)), args, kwargs)
    263 def __bool__(self):
    264     return True

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe2 in position 4095: unexpected end of data

I've narrowed it down to a few things but I can't put my finger on the exact reason. It could be something wrong with my data frame but I can't find anything that stands out.

I've tried pairing down the model as much as possible and here's my model setup:

# Define input (X) and target (y)
X = data[feature_names].values
# y = data["entropy"].values
y = data["entropy_kde_XY"].values
# y = data["entropy_theoretical_XY"].values


model = PySRRegressor(
    model_selection="best",
    run_id=f'Bi-Skew-Normal - {datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S.%f")[:-4]}',
    niterations=10000000,
    binary_operators=["+", "-", "*", "/"],
    unary_operators=["log", "sqrt", "square","exp","cube","cbrt"
                     # , "sin", "cos", "tan"
                     ],
    nested_constraints = {
    "log": {"log": 0},
    "sqrt": {"sqrt": 0},
    "square": {"square": 0},
    "exp": {"exp": 0},
    "cube": {"cube": 0},
    "cbrt": {"cbrt": 0}
    },
    populations=48,
    ncycles_per_iteration=5000,
    # batching=True,
    # weight_optimize=.005,
    # turbo=True,
    
    maxsize=26,
    warm_start=True,
    temp_equation_file=False,
    output_directory=r"C:/Users/arose/OneDrive/Desktop/output/BivariateSkewNormal",
    verbosity=1,
    timeout_in_seconds=60,
    complexity_of_constants=1,
    # logger_spec=logger_spec
    # early_stop_condition="f(loss, complexity) = (loss <= 5.526e-15) && (complexity < 6)",print_precision=24
)

# model.weight_optimize = .005
# model.maxsize = 26
# model.ncycles_per_iteration = 5000
# model.timeout_in_seconds = 120
# model.print_precision = 16
# model.model_selection = 'accuracy'
# model.unary_operators = ["log", "square"]
# model.populations = 96



st = time.time()
model.fit(X, y, variable_names=feature_names)
runtime = round(time.time() - st, 2)
print(f"Elapsed time: {runtime} seconds")

Any expertise you can provide would be immensely helpful. I'm going to take a deeper dive into my data and see if I can find anything that stands out in my data.

[MilesCranmer]

Moving to #945