def setUp(self):
self.SymbolTree = SymbolTree
self.pset = SymbolSet()
from sklearn.datasets import load_boston
data = load_boston()
x = data["data"]
y = data["target"]
# No = Normalizer()
# y=y/max(y)
# x = No.fit_transform(x)
self.x = x
self.y = y
# self.pset.add_features(x, y, )
self.pset.add_features(x, y, x_group=[[1, 2], [4, 5]])
self.pset.add_constants([6, 3, 4],
c_dim=[dless, dless, dless],
c_prob=None)
self.pset.add_operations(power_categories=(2, 3, 0.5),
categories=("Add", "Mul", "Self", "Abs"),
self_categories=None)
from sklearn.metrics import r2_score, mean_squared_error
self.cp = CalculatePrecisionSet(self.pset,
scoring=[r2_score, mean_squared_error],
score_pen=[1, -1],
filter_warning=True)
def fit(self, X=None, y=None, c=None, x_group=None, pset=None):
"""
If pset is None, one simple pset are generate with no dimension calculation, But just with x_group.\n
If need more self-definition, use one defined SymbolSet object to pset.\n
Examples:
pset = SymbolSet()\n
pset.add_features_and_constants(...)\n
pset.add_operations(...)\n
...\n
...SymbolLearning().fit(pset=pset)\n
Parameters
----------
X:np.ndarray
y:np.ndarray
c:list of float
x_group:list of list
Group of x.\n
See Also pset.add_features_and_constants
pset:SymbolSet
See Also SymbolSet
"""
if pset is None:
pset = SymbolSet()
pset.add_features_and_constants(X,
y,
c,
x_dim=1,
y_dim=1,
c_dim=1,
x_prob=None,
c_prob=None,
x_group=x_group,
feature_name=None)
pset.add_operations(power_categories=(2, 3, 0.5),
categories=("Add", "Mul", "Sub", "Div"))
self.loop = self.loop(pset, *self.args, **self.kwargs)
hall = self.loop.run()
self.best_one = hall.items[0]
try:
expr = general_expr(self.best_one.coef_expr, self.loop.cpset)
self.expr_type = "single"
except (RecursionError, RuntimeWarning):
expr = self.best_one.coef_expr
self.expr_type = "group"
self.expr = expr
self.y_dim = self.best_one.y_dim
self.fitness = self.best_one.fitness.values[0]
c = [6, 3, 4]
# unit
from sympy.physics.units import kg
x_u = [kg] * 13
y_u = kg
c_u = [dless, dless, dless]
x, x_dim = Dim.convert_x(x, x_u, target_units=None, unit_system="SI")
y, y_dim = Dim.convert_xi(y, y_u)
c, c_dim = Dim.convert_x(c, c_u)
t = time.time()
# symbolset
pset0 = SymbolSet()
pset0.add_features(x,
y,
x_dim=x_dim,
y_dim=y_dim,
x_group=[[1, 2], [3, 4, 5]])
pset0.add_constants(c, c_dim=c_dim, c_prob=None)
pset0.add_operations(power_categories=(2, 3, 0.5),
categories=("Add", "Mul", "Sub", "Div", "exp"),
self_categories=None)
random.seed(0)
z = time.time()
sl = [SymbolTree.genGrow(pset0, 3, 4) for _ in range(100)]
a = time.time()
sl = [compile_context(sli, pset0.context, pset0.gro_ter_con) for sli in sl]
import sympy
from featurebox.symbol.base import SymbolSet, SymbolTree
import numpy as np
from featurebox.symbol.calculation.translate import general_expr_dict, compile_context
if __name__ == "__main__":
x = np.full((10, 4), fill_value=0.1)
y = np.ones((10, ))
height = 2
# GVP
group = 2
pset = SymbolSet()
pset.add_features(
x,
y,
x_group=group,
)
pset.add_accumulative_operation(categories=("MAdd", "MMul", "MSub", "MDiv",
"Conv", "Self"),
special_prob={
"MAdd": 0.16,
"MMul": 0.16,
"MSub": 0.16,
"MDiv": 0.16,
"Conv": 0.16,
"Self": 0.16
})