def test_pandamodels_dev_mode():
from julia import Main
from julia import Pkg
from julia import Base
if Base.find_package("PandaModels"):
# remove PandaModels to reinstall it
Pkg.rm("PandaModels")
Pkg.resolve()
Pkg.Registry.update()
Pkg.add("PandaModels")
print("installing dev mode is a slow process!")
Pkg.resolve()
Pkg.develop("PandaModels")
# add pandamodels dependencies: slow process
Pkg.instantiate()
Pkg.build()
Pkg.resolve()
print("dev mode of PandaModels is added to julia packages")
try:
Pkg.activate("PandaModels")
Main.using("PandaModels")
print("using PandaModels in its dev mode!")
except ImportError:
# assert False
raise ImportError("cannot use PandaModels in its dev mode")
# activate julia base mode
Pkg.activate()
Pkg.free("PandaModels")
Pkg.resolve()
def _call_pandamodels(buffer_file, julia_file, dev_mode): # pragma: no cover
try:
import julia
from julia import Main
from julia import Pkg
from julia import Base
except ImportError:
raise ImportError(
"Please install pyjulia properly to run pandapower with PandaModels.jl."
)
try:
julia.Julia()
except:
raise UserWarning(
"Could not connect to julia, please check that Julia is installed and pyjulia is correctly configured"
)
if not Base.find_package("PandaModels"):
logger.info(
"PandaModels.jl is not installed in julia. It is added now!")
Pkg.Registry.update()
Pkg.add("PandaModels")
if dev_mode:
logger.info("installing dev mode is a slow process!")
Pkg.resolve()
Pkg.develop("PandaModels")
# add pandamodels dependencies: slow process
Pkg.instantiate()
Pkg.build()
Pkg.resolve()
logger.info("Successfully added PandaModels")
if dev_mode:
Pkg.develop("PandaModels")
Pkg.build()
Pkg.resolve()
Pkg.activate("PandaModels")
try:
Main.using("PandaModels")
except ImportError:
raise ImportError("cannot use PandaModels")
Main.buffer_file = buffer_file
result_pm = Main.eval(julia_file + "(buffer_file)")
# if dev_mode:
# Pkg.activate()
# Pkg.free("PandaModels")
# Pkg.resolve()
return result_pm
def install(julia_project=None): # pragma: no cover
import julia
julia.install()
julia_project = _get_julia_project(julia_project)
init_julia()
from julia import Pkg
Pkg.activate(f"{_escape_filename(julia_project)}")
Pkg.update()
Pkg.instantiate()
Pkg.precompile()
warnings.warn(
"It is recommended to restart Python after installing PySR's dependencies,"
" so that the Julia environment is properly initialized.")
def run_jump_model(self, dfm, data, run_uuid, bau=False):
profiler = Profiler()
time_dict = dict()
name = 'reopt' if not bau else 'reopt_bau'
reopt_inputs = dfm['reopt_inputs'] if not bau else dfm['reopt_inputs_bau']
self.data = data
self.run_uuid = data['outputs']['Scenario']['run_uuid']
self.user_uuid = data['outputs']['Scenario'].get('user_uuid')
if platform.system() == "Darwin":
ext = ".dylib"
elif platform.system() == "Windows":
ext = ".dll"
else:
ext = ".so" # if platform.system() == "Linux":
julia_img_file = os.path.join("julia_envs", "Xpress",
"JuliaXpressSysimage" + ext)
logger.info("Running JuMP model ...")
try:
if os.path.isfile(julia_img_file):
# TODO: clean up this try/except block
logger.info("Found Julia image file {}.".format(julia_img_file))
t_start = time.time()
api = LibJulia.load()
api.sysimage = julia_img_file
api.init_julia()
from julia import Main
time_dict["pyjulia_start_seconds"] = time.time() - t_start
else:
t_start = time.time()
j = julia.Julia()
from julia import Main
time_dict["pyjulia_start_seconds"] = time.time() - t_start
t_start = time.time()
Main.using("Pkg")
from julia import Pkg
time_dict["pyjulia_pkg_seconds"] = time.time() - t_start
if os.environ.get("SOLVER") == "xpress":
t_start = time.time()
Pkg.activate("./julia_envs/Xpress/")
time_dict["pyjulia_activate_seconds"] = time.time() - t_start
try:
t_start = time.time()
Main.include("reo/src/reopt_xpress_model.jl")
time_dict["pyjulia_include_model_seconds"] = time.time(
) - t_start
except ImportError:
# should only need to instantiate once
Pkg.instantiate()
Main.include("reo/src/reopt_xpress_model.jl")
t_start = time.time()
if bau:
model = Main.reopt_model(
float(data["inputs"]["Scenario"]["timeout_seconds"]),
float(data["inputs"]["Scenario"]
["optimality_tolerance_bau"]))
else:
model = Main.reopt_model(
float(data["inputs"]["Scenario"]["timeout_seconds"]),
float(data["inputs"]["Scenario"]
["optimality_tolerance_techs"]))
time_dict["pyjulia_make_model_seconds"] = time.time() - t_start
elif os.environ.get("SOLVER") == "cbc":
t_start = time.time()
Pkg.activate("./julia_envs/Cbc/")
time_dict["pyjulia_activate_seconds"] = time.time() - t_start
t_start = time.time()
Main.include("reo/src/reopt_cbc_model.jl")
time_dict["pyjulia_include_model_seconds"] = time.time() - t_start
t_start = time.time()
model = Main.reopt_model(
float(data["inputs"]["Scenario"]["timeout_seconds"]),
float(data["inputs"]["Scenario"]["optimality_tolerance_bau"]))
time_dict["pyjulia_make_model_seconds"] = time.time() - t_start
elif os.environ.get("SOLVER") == "scip":
t_start = time.time()
Pkg.activate("./julia_envs/SCIP/")
time_dict["pyjulia_activate_seconds"] = time.time() - t_start
t_start = time.time()
Main.include("reo/src/reopt_scip_model.jl")
time_dict["pyjulia_include_model_seconds"] = time.time() - t_start
t_start = time.time()
model = Main.reopt_model(
float(data["inputs"]["Scenario"]["timeout_seconds"]),
float(data["inputs"]["Scenario"]["optimality_tolerance_bau"]))
time_dict["pyjulia_make_model_seconds"] = time.time() - t_start
else:
raise REoptFailedToStartError(
message=
"The environment variable SOLVER must be set to one of [xpress, cbc, scip].",
run_uuid=self.run_uuid,
user_uuid=self.user_uuid)
if bau or not data["inputs"]["Scenario"]["use_decomposition_model"]:
t_start = time.time()
Main.include("reo/src/reopt.jl")
time_dict["pyjulia_include_reopt_seconds"] = time.time() - t_start
t_start = time.time()
results = Main.reopt(model, reopt_inputs)
time_dict["pyjulia_run_reopt_seconds"] = time.time() - t_start
else:
t_start = time.time()
Main.include("reo/src/reopt_decomposed.jl")
time_dict["pyjulia_include_reopt_seconds"] = time.time() - t_start
t_start = time.time()
results = run_decomposed_model(data, model, reopt_inputs)
time_dict["pyjulia_run_reopt_seconds"] = time.time() - t_start
results = scrub_numpy_arrays_from_dict(results)
results.update(time_dict)
except Exception as e:
if isinstance(e, REoptFailedToStartError):
raise e
elif "DimensionMismatch" in e.args[
0]: # JuMP may mishandle a timeout when no feasible solution is returned
msg = "Optimization exceeded timeout: {} seconds.".format(
data["inputs"]["Scenario"]["timeout_seconds"])
logger.info(msg)
raise OptimizationTimeout(task=name,
message=msg,
run_uuid=self.run_uuid,
user_uuid=self.user_uuid)
exc_type, exc_value, exc_traceback = sys.exc_info()
print(exc_type)
print(exc_value)
print(exc_traceback)
logger.error("REopt.py raise unexpected error: UUID: " +
str(self.run_uuid))
raise UnexpectedError(exc_type,
exc_value,
traceback.format_tb(exc_traceback),
task=name,
run_uuid=self.run_uuid,
user_uuid=self.user_uuid)
else:
status = results["status"]
logger.info("REopt run successful. Status {}".format(status))
if bau:
dfm['results_bau'] = results # will be flat dict
else:
dfm['results'] = results
if status.strip().lower() == 'timed-out':
msg = "Optimization exceeded timeout: {} seconds.".format(
data["inputs"]["Scenario"]["timeout_seconds"])
logger.info(msg)
raise OptimizationTimeout(task=name,
message=msg,
run_uuid=self.run_uuid,
user_uuid=self.user_uuid)
elif status.strip().lower() != 'optimal':
logger.error(
"REopt status not optimal. Raising NotOptimal Exception.")
raise NotOptimal(task=name,
run_uuid=self.run_uuid,
status=status.strip(),
user_uuid=self.user_uuid)
profiler.profileEnd()
ModelManager.updateModel('ProfileModel',
{name + '_seconds': profiler.getDuration()},
run_uuid)
# reduce the amount data being transferred between tasks
if bau:
del dfm['reopt_inputs_bau']
else:
del dfm['reopt_inputs']
return dfm
def pysr(
X,
y,
weights=None,
binary_operators=None,
unary_operators=None,
procs=cpu_count(),
loss="L2DistLoss()",
populations=20,
niterations=100,
ncyclesperiteration=300,
alpha=0.1,
annealing=False,
fractionReplaced=0.10,
fractionReplacedHof=0.10,
npop=1000,
parsimony=1e-4,
migration=True,
hofMigration=True,
shouldOptimizeConstants=True,
topn=10,
weightAddNode=1,
weightInsertNode=3,
weightDeleteNode=3,
weightDoNothing=1,
weightMutateConstant=10,
weightMutateOperator=1,
weightRandomize=1,
weightSimplify=0.002,
perturbationFactor=1.0,
extra_sympy_mappings=None,
extra_torch_mappings=None,
extra_jax_mappings=None,
equation_file=None,
verbosity=1e9,
progress=None,
maxsize=20,
fast_cycle=False,
maxdepth=None,
variable_names=None,
batching=False,
batchSize=50,
select_k_features=None,
warmupMaxsizeBy=0.0,
constraints=None,
useFrequency=True,
tempdir=None,
delete_tempfiles=True,
julia_project=None,
update=True,
temp_equation_file=False,
output_jax_format=False,
output_torch_format=False,
optimizer_algorithm="BFGS",
optimizer_nrestarts=3,
optimize_probability=1.0,
optimizer_iterations=10,
tournament_selection_n=10,
tournament_selection_p=1.0,
denoise=False,
Xresampled=None,
precision=32,
multithreading=None,
**kwargs,
):
"""Run symbolic regression to fit f(X[i, :]) ~ y[i] for all i.
Note: most default parameters have been tuned over several example
equations, but you should adjust `niterations`,
`binary_operators`, `unary_operators` to your requirements.
You can view more detailed explanations of the options on the
[options page](https://pysr.readthedocs.io/en/latest/docs/options/) of the documentation.
:param X: 2D array. Rows are examples, columns are features. If pandas DataFrame, the columns are used for variable names (so make sure they don't contain spaces).
:type X: np.ndarray/pandas.DataFrame
:param y: 1D array (rows are examples) or 2D array (rows are examples, columns are outputs). Putting in a 2D array will trigger a search for equations for each feature of y.
:type y: np.ndarray
:param weights: same shape as y. Each element is how to weight the mean-square-error loss for that particular element of y.
:type weights: np.ndarray
:param binary_operators: List of strings giving the binary operators in Julia's Base. Default is ["+", "-", "*", "/",].
:type binary_operators: list
:param unary_operators: Same but for operators taking a single scalar. Default is [].
:type unary_operators: list
:param procs: Number of processes (=number of populations running).
:type procs: int
:param loss: String of Julia code specifying the loss function. Can either be a loss from LossFunctions.jl, or your own loss written as a function. Examples of custom written losses include: `myloss(x, y) = abs(x-y)` for non-weighted, or `myloss(x, y, w) = w*abs(x-y)` for weighted. Among the included losses, these are as follows. Regression: `LPDistLoss{P}()`, `L1DistLoss()`, `L2DistLoss()` (mean square), `LogitDistLoss()`, `HuberLoss(d)`, `L1EpsilonInsLoss(ϵ)`, `L2EpsilonInsLoss(ϵ)`, `PeriodicLoss(c)`, `QuantileLoss(τ)`. Classification: `ZeroOneLoss()`, `PerceptronLoss()`, `L1HingeLoss()`, `SmoothedL1HingeLoss(γ)`, `ModifiedHuberLoss()`, `L2MarginLoss()`, `ExpLoss()`, `SigmoidLoss()`, `DWDMarginLoss(q)`.
:type loss: str
:param populations: Number of populations running.
:type populations: int
:param niterations: Number of iterations of the algorithm to run. The best equations are printed, and migrate between populations, at the end of each.
:type niterations: int
:param ncyclesperiteration: Number of total mutations to run, per 10 samples of the population, per iteration.
:type ncyclesperiteration: int
:param alpha: Initial temperature.
:type alpha: float
:param annealing: Whether to use annealing. You should (and it is default).
:type annealing: bool
:param fractionReplaced: How much of population to replace with migrating equations from other populations.
:type fractionReplaced: float
:param fractionReplacedHof: How much of population to replace with migrating equations from hall of fame.
:type fractionReplacedHof: float
:param npop: Number of individuals in each population
:type npop: int
:param parsimony: Multiplicative factor for how much to punish complexity.
:type parsimony: float
:param migration: Whether to migrate.
:type migration: bool
:param hofMigration: Whether to have the hall of fame migrate.
:type hofMigration: bool
:param shouldOptimizeConstants: Whether to numerically optimize constants (Nelder-Mead/Newton) at the end of each iteration.
:type shouldOptimizeConstants: bool
:param topn: How many top individuals migrate from each population.
:type topn: int
:param perturbationFactor: Constants are perturbed by a max factor of (perturbationFactor*T + 1). Either multiplied by this or divided by this.
:type perturbationFactor: float
:param weightAddNode: Relative likelihood for mutation to add a node
:type weightAddNode: float
:param weightInsertNode: Relative likelihood for mutation to insert a node
:type weightInsertNode: float
:param weightDeleteNode: Relative likelihood for mutation to delete a node
:type weightDeleteNode: float
:param weightDoNothing: Relative likelihood for mutation to leave the individual
:type weightDoNothing: float
:param weightMutateConstant: Relative likelihood for mutation to change the constant slightly in a random direction.
:type weightMutateConstant: float
:param weightMutateOperator: Relative likelihood for mutation to swap an operator.
:type weightMutateOperator: float
:param weightRandomize: Relative likelihood for mutation to completely delete and then randomly generate the equation
:type weightRandomize: float
:param weightSimplify: Relative likelihood for mutation to simplify constant parts by evaluation
:type weightSimplify: float
:param equation_file: Where to save the files (.csv separated by |)
:type equation_file: str
:param verbosity: What verbosity level to use. 0 means minimal print statements.
:type verbosity: int
:param progress: Whether to use a progress bar instead of printing to stdout.
:type progress: bool
:param maxsize: Max size of an equation.
:type maxsize: int
:param maxdepth: Max depth of an equation. You can use both maxsize and maxdepth. maxdepth is by default set to = maxsize, which means that it is redundant.
:type maxdepth: int
:param fast_cycle: (experimental) - batch over population subsamples. This is a slightly different algorithm than regularized evolution, but does cycles 15% faster. May be algorithmically less efficient.
:type fast_cycle: bool
:param variable_names: a list of names for the variables, other than "x0", "x1", etc.
:type variable_names: list
:param batching: whether to compare population members on small batches during evolution. Still uses full dataset for comparing against hall of fame.
:type batching: bool
:param batchSize: the amount of data to use if doing batching.
:type batchSize: int
:param select_k_features: whether to run feature selection in Python using random forests, before passing to the symbolic regression code. None means no feature selection; an int means select that many features.
:type select_k_features: None/int
:param warmupMaxsizeBy: whether to slowly increase max size from a small number up to the maxsize (if greater than 0). If greater than 0, says the fraction of training time at which the current maxsize will reach the user-passed maxsize.
:type warmupMaxsizeBy: float
:param constraints: dictionary of int (unary) or 2-tuples (binary), this enforces maxsize constraints on the individual arguments of operators. E.g., `'pow': (-1, 1)` says that power laws can have any complexity left argument, but only 1 complexity exponent. Use this to force more interpretable solutions.
:type constraints: dict
:param useFrequency: whether to measure the frequency of complexities, and use that instead of parsimony to explore equation space. Will naturally find equations of all complexities.
:type useFrequency: bool
:param tempdir: directory for the temporary files
:type tempdir: str/None
:param delete_tempfiles: whether to delete the temporary files after finishing
:type delete_tempfiles: bool
:param julia_project: a Julia environment location containing a Project.toml (and potentially the source code for SymbolicRegression.jl). Default gives the Python package directory, where a Project.toml file should be present from the install.
:type julia_project: str/None
:param update: Whether to automatically update Julia packages.
:type update: bool
:param temp_equation_file: Whether to put the hall of fame file in the temp directory. Deletion is then controlled with the delete_tempfiles argument.
:type temp_equation_file: bool
:param output_jax_format: Whether to create a 'jax_format' column in the output, containing jax-callable functions and the default parameters in a jax array.
:type output_jax_format: bool
:param output_torch_format: Whether to create a 'torch_format' column in the output, containing a torch module with trainable parameters.
:type output_torch_format: bool
:param tournament_selection_n: Number of expressions to consider in each tournament.
:type tournament_selection_n: int
:param tournament_selection_p: Probability of selecting the best expression in each tournament. The probability will decay as p*(1-p)^n for other expressions, sorted by loss.
:type tournament_selection_p: float
:param denoise: Whether to use a Gaussian Process to denoise the data before inputting to PySR. Can help PySR fit noisy data.
:type denoise: bool
:param precision: What precision to use for the data. By default this is 32 (float32), but you can select 64 or 16 as well.
:type precision: int
:param multithreading: Use multithreading instead of distributed backend. Default is yes. Using procs=0 will turn off both.
:type multithreading: bool
:param **kwargs: Other options passed to SymbolicRegression.Options, for example, if you modify SymbolicRegression.jl to include additional arguments.
:type **kwargs: dict
:returns: Results dataframe, giving complexity, MSE, and equations (as strings), as well as functional forms. If list, each element corresponds to a dataframe of equations for each output.
:type: pd.DataFrame/list
"""
global already_ran
if binary_operators is None:
binary_operators = "+ * - /".split(" ")
if unary_operators is None:
unary_operators = []
if extra_sympy_mappings is None:
extra_sympy_mappings = {}
if variable_names is None:
variable_names = []
if constraints is None:
constraints = {}
if multithreading is None:
# Default is multithreading=True, unless explicitly set,
# or procs is set to 0 (serial mode).
multithreading = procs != 0
global Main
if Main is None:
if multithreading:
os.environ["JULIA_NUM_THREADS"] = str(procs)
Main = init_julia()
buffer_available = "buffer" in sys.stdout.__dir__()
if progress is not None:
if progress and not buffer_available:
warnings.warn(
"Note: it looks like you are running in Jupyter. The progress bar will be turned off."
)
progress = False
else:
progress = buffer_available
assert optimizer_algorithm in ["NelderMead", "BFGS"]
assert tournament_selection_n < npop
if isinstance(X, pd.DataFrame):
variable_names = list(X.columns)
X = np.array(X)
if len(X.shape) == 1:
X = X[:, None]
assert not isinstance(y, pd.DataFrame)
if len(variable_names) == 0:
variable_names = [f"x{i}" for i in range(X.shape[1])]
if extra_jax_mappings is not None:
for value in extra_jax_mappings.values():
if not isinstance(value, str):
raise NotImplementedError(
"extra_jax_mappings must have keys that are strings! e.g., {sympy.sqrt: 'jnp.sqrt'}."
)
if extra_torch_mappings is not None:
for value in extra_jax_mappings.values():
if not callable(value):
raise NotImplementedError(
"extra_torch_mappings must be callable functions! e.g., {sympy.sqrt: torch.sqrt}."
)
use_custom_variable_names = len(variable_names) != 0
# TODO: this is always true.
_check_assertions(
X,
binary_operators,
unary_operators,
use_custom_variable_names,
variable_names,
weights,
y,
)
if len(X) > 10000 and not batching:
warnings.warn(
"Note: you are running with more than 10,000 datapoints. You should consider turning on batching (https://pysr.readthedocs.io/en/latest/docs/options/#batching). You should also reconsider if you need that many datapoints. Unless you have a large amount of noise (in which case you should smooth your dataset first), generally < 10,000 datapoints is enough to find a functional form with symbolic regression. More datapoints will lower the search speed."
)
if maxsize > 40:
warnings.warn(
"Note: Using a large maxsize for the equation search will be exponentially slower and use significant memory. You should consider turning `useFrequency` to False, and perhaps use `warmupMaxsizeBy`."
)
if maxsize < 7:
raise NotImplementedError("PySR requires a maxsize of at least 7")
X, selection = _handle_feature_selection(X, select_k_features, y,
variable_names)
if maxdepth is None:
maxdepth = maxsize
if isinstance(binary_operators, str):
binary_operators = [binary_operators]
if isinstance(unary_operators, str):
unary_operators = [unary_operators]
if len(y.shape) == 1 or (len(y.shape) == 2 and y.shape[1] == 1):
multioutput = False
nout = 1
y = y.reshape(-1)
elif len(y.shape) == 2:
multioutput = True
nout = y.shape[1]
else:
raise NotImplementedError("y shape not supported!")
if denoise:
if weights is not None:
raise NotImplementedError(
"No weights for denoising - the weights are learned.")
if Xresampled is not None:
# Select among only the selected features:
if isinstance(Xresampled, pd.DataFrame):
# Handle Xresampled is pandas dataframe
if selection is not None:
Xresampled = Xresampled[[
variable_names[i] for i in selection
]]
else:
Xresampled = Xresampled[variable_names]
Xresampled = np.array(Xresampled)
else:
if selection is not None:
Xresampled = Xresampled[:, selection]
if multioutput:
y = np.stack(
[
_denoise(X, y[:, i], Xresampled=Xresampled)[1]
for i in range(nout)
],
axis=1,
)
if Xresampled is not None:
X = Xresampled
else:
X, y = _denoise(X, y, Xresampled=Xresampled)
julia_project = _get_julia_project(julia_project)
tmpdir = Path(tempfile.mkdtemp(dir=tempdir))
if temp_equation_file:
equation_file = tmpdir / "hall_of_fame.csv"
elif equation_file is None:
date_time = datetime.now().strftime("%Y-%m-%d_%H%M%S.%f")[:-3]
equation_file = "hall_of_fame_" + date_time + ".csv"
_create_inline_operators(binary_operators=binary_operators,
unary_operators=unary_operators)
_handle_constraints(
binary_operators=binary_operators,
unary_operators=unary_operators,
constraints=constraints,
)
una_constraints = [constraints[op] for op in unary_operators]
bin_constraints = [constraints[op] for op in binary_operators]
try:
# TODO: is this needed since Julia now prints directly to stdout?
term_width = shutil.get_terminal_size().columns
except:
_, term_width = subprocess.check_output(["stty", "size"]).split()
if not already_ran:
from julia import Pkg
Pkg.activate(f"{_escape_filename(julia_project)}")
try:
if update:
Pkg.resolve()
Pkg.instantiate()
else:
Pkg.instantiate()
except RuntimeError as e:
raise ImportError(f"""
Required dependencies are not installed or built. Run the following code in the Python REPL:
>>> import pysr
>>> pysr.install()
Tried to activate project {julia_project} but failed.""") from e
Main.eval("using SymbolicRegression")
Main.plus = Main.eval("(+)")
Main.sub = Main.eval("(-)")
Main.mult = Main.eval("(*)")
Main.pow = Main.eval("(^)")
Main.div = Main.eval("(/)")
Main.custom_loss = Main.eval(loss)
mutationWeights = [
float(weightMutateConstant),
float(weightMutateOperator),
float(weightAddNode),
float(weightInsertNode),
float(weightDeleteNode),
float(weightSimplify),
float(weightRandomize),
float(weightDoNothing),
]
options = Main.Options(
binary_operators=Main.eval(
str(tuple(binary_operators)).replace("'", "")),
unary_operators=Main.eval(
str(tuple(unary_operators)).replace("'", "")),
bin_constraints=bin_constraints,
una_constraints=una_constraints,
parsimony=float(parsimony),
loss=Main.custom_loss,
alpha=float(alpha),
maxsize=int(maxsize),
maxdepth=int(maxdepth),
fast_cycle=fast_cycle,
migration=migration,
hofMigration=hofMigration,
fractionReplacedHof=float(fractionReplacedHof),
shouldOptimizeConstants=shouldOptimizeConstants,
hofFile=_escape_filename(equation_file),
npopulations=int(populations),
optimizer_algorithm=optimizer_algorithm,
optimizer_nrestarts=int(optimizer_nrestarts),
optimize_probability=float(optimize_probability),
optimizer_iterations=int(optimizer_iterations),
perturbationFactor=float(perturbationFactor),
annealing=annealing,
batching=batching,
batchSize=int(min([batchSize, len(X)]) if batching else len(X)),
mutationWeights=mutationWeights,
warmupMaxsizeBy=float(warmupMaxsizeBy),
useFrequency=useFrequency,
npop=int(npop),
ns=int(tournament_selection_n),
probPickFirst=float(tournament_selection_p),
ncyclesperiteration=int(ncyclesperiteration),
fractionReplaced=float(fractionReplaced),
topn=int(topn),
verbosity=int(verbosity),
progress=progress,
terminal_width=int(term_width),
**kwargs,
)
np_dtype = {16: np.float16, 32: np.float32, 64: np.float64}[precision]
Main.X = np.array(X, dtype=np_dtype).T
if len(y.shape) == 1:
Main.y = np.array(y, dtype=np_dtype)
else:
Main.y = np.array(y, dtype=np_dtype).T
if weights is not None:
if len(weights.shape) == 1:
Main.weights = np.array(weights, dtype=np_dtype)
else:
Main.weights = np.array(weights, dtype=np_dtype).T
else:
Main.weights = None
cprocs = 0 if multithreading else procs
raw_julia_output = Main.EquationSearch(
Main.X,
Main.y,
weights=Main.weights,
niterations=int(niterations),
varMap=(variable_names if selection is None else
[variable_names[i] for i in selection]),
options=options,
numprocs=int(cprocs),
multithreading=bool(multithreading),
)
_set_globals(
X=X,
equation_file=equation_file,
variable_names=variable_names,
extra_sympy_mappings=extra_sympy_mappings,
extra_torch_mappings=extra_torch_mappings,
extra_jax_mappings=extra_jax_mappings,
output_jax_format=output_jax_format,
output_torch_format=output_torch_format,
multioutput=multioutput,
nout=nout,
selection=selection,
raw_julia_output=raw_julia_output,
)
equations = get_hof(
equation_file=equation_file,
n_features=X.shape[1],
variable_names=variable_names,
output_jax_format=output_jax_format,
output_torch_format=output_torch_format,
selection=selection,
extra_sympy_mappings=extra_sympy_mappings,
extra_jax_mappings=extra_jax_mappings,
extra_torch_mappings=extra_torch_mappings,
multioutput=multioutput,
nout=nout,
)
if delete_tempfiles:
shutil.rmtree(tmpdir)
already_ran = True
return equations