def setUp(self):
self.SymbolTree = SymbolTree
self.pset = SymbolSet()
from sklearn.datasets import fetch_california_housing
data = fetch_california_housing()
x = data["data"][:100]
y = data["target"][:100]
# 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 setUp(self):
self.SymbolTree = SymbolTree
self.pset = SymbolSet()
from sklearn.datasets import load_boston
data = load_boston()
x = data["data"]
y = data["target"]
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", "Neg", "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],
dim_type=None,
filter_warning=True)
def __init__(self, pset, pop=500, gen=20, mutate_prob=0.5, mate_prob=0.8, hall=1, re_hall=1,
re_Tree=None, initial_min=None, initial_max=3, max_value=5,
scoring=(r2_score,), score_pen=(1,), filter_warning=True, cv=1,
add_coef=True, inter_add=True, inner_add=False, vector_add=False, out_add=False, flat_add=False,
cal_dim=False, dim_type=None, fuzzy=False, n_jobs=1, batch_size=40,
random_state=None, stats=None, verbose=True, migrate_prob=0,
tq=True, store=False, personal_map=False, stop_condition=None, details=False, classification=False,
score_object="y", sub_mu_max=1, limit_type="h_bgp", batch_para=False):
"""
Parameters
----------
pset:SymbolSet
the feature x and target y and others should have been added.
pop: int
number of population.
gen: int
number of generation.
mutate_prob:float
probability of mutate.
mate_prob:float
probability of mate(crossover).
initial_max:int
max initial size of expression when first producing.
initial_min : None,int
min initial size of expression when first producing.
max_value:int
max size of expression.
limit_type: "height" or "length",","h_bgp"
limitation type for max_value, but don't affect initial_max, initial_min.
hall:int,>=1
number of HallOfFame (elite) to maintain.
re_hall:None or int>=2
Notes: only valid when hall
number of HallOfFame to add to next generation.
re_Tree: int
number of new features to add to next generation.
0 is false to add.
personal_map:bool or "auto"
"auto" is using 'premap' and with auto refresh the 'premap' with individual.\n
True is just using constant 'premap'.\n
False is just use the prob of terminals.
scoring: list of Callable, default is [sklearn.metrics.r2_score,]
See Also ``sklearn.metrics``
score_pen: tuple of 1, -1 or float but 0.
>0 : max problem, best is positive, worse -np.inf.
<0 : min problem, best is negative, worse np.inf.
Notes:
if multiply score method, the scores must be turn to same dimension in prepossessing
or weight by score_pen. Because the all the selection are stand on the mean(w_i*score_i)
Examples::
scoring = [r2_score,]
score_pen= [1,]
cv:sklearn.model_selection._split._BaseKFold,int
the shuffler must be False,
default=1 means no cv.
filter_warning:bool
filter warning or not.
add_coef:bool
add coef in expression or not.
inter_add：bool
add intercept constant or not.
inner_add:bool
add inner coefficients or not.
out_add:bool
add out coefficients or not.
flat_add:bool
add flat coefficients or not.
n_jobs:int
default 1, advise 6.
batch_size:int
default 40, depend of machine.
random_state:int
None,int.
cal_dim:bool
escape the dim calculation.
dim_type:Dim or None or list of Dim
"coef": af(x)+b. a,b have dimension,f(x)'s dimension is not dnan. \n
"integer": af(x)+b. f(x) is with integer dimension. \n
[Dim1,Dim2]: f(x)'s dimension in list. \n
Dim: f(x) ~= Dim. (see fuzzy) \n
Dim: f(x) == Dim. \n
None: f(x) == pset.y_dim
fuzzy:bool
choose the dim with same base with dim_type, such as m,m^2,m^3.
stats:dict
details of logbook to show. \n
Map:\n
values
= {"max": np.max, "mean": np.mean, "min": np.mean, "std": np.std, "sum": np.sum}
keys
= {\n
"fitness": just see fitness[0], \n
"fitness_dim_max": max problem, see fitness with demand dim,\n
"fitness_dim_min": min problem, see fitness with demand dim,\n
"dim_is_target": demand dim,\n
"coef": dim is True, coef have dim, \n
"integer": dim is integer, \n
...
}
if stats is None, default is:
for cal_dim=True:
stats = {"fitness_dim_max": ("max",), "dim_is_target": ("sum",)}
for cal_dim=False
stats = {"fitness": ("max",)}
if self-definition, the key is func to get attribute of each ind.
Examples::
def func(ind):
return ind.fitness[0]
stats = {func: ("mean",), "dim_is_target": ("sum",)}
verbose:bool
print verbose logbook or not.
tq:bool
print progress bar or not.
store:bool or path
bool or path.
stop_condition:callable
stop condition on the best ind of hall, which return bool,the true means stop loop.
Examples::
def func(ind):
c = ind.fitness.values[0]>=0.90
return c
details:bool
return expr and predict_y or not.
classification: bool
classification or not.
score_object:
score by y or delta y (for implicit function).
"""
super(BaseLoop, self).__init__()
assert initial_max <= max_value, "the initial size of expression should less than max_value limitation"
if cal_dim:
assert all(
[isinstance(i, Dim) for i in pset.dim_ter_con.values()]), \
"all import dim of pset should be Dim object."
self.details = details
self.max_value = max_value
self.pop = pop
self.gen = gen
self.mutate_prob = mutate_prob
self.mate_prob = mate_prob
self.migrate_prob = migrate_prob
self.verbose = verbose
self.cal_dim = cal_dim
self.re_hall = re_hall
self.re_Tree = re_Tree
self.store = store
self.limit_type = limit_type
self.data_all = []
self.personal_map = personal_map
self.stop_condition = stop_condition
self.population = []
self.rand_state = None
self.random_state = random_state
self.sub_mu_max = sub_mu_max
self.population_next = []
self.cpset = CalculatePrecisionSet(pset, scoring=scoring, score_pen=score_pen,
filter_warning=filter_warning, cal_dim=cal_dim,
add_coef=add_coef, inter_add=inter_add, inner_add=inner_add,
vector_add=vector_add, out_add=out_add, flat_add=flat_add, cv=cv,
n_jobs=n_jobs, batch_size=batch_size, tq=tq,
fuzzy=fuzzy, dim_type=dim_type, details=details,
classification=classification, score_object=score_object,
batch_para=batch_para
)
Fitness_ = newclass.create("Fitness_", Fitness, weights=score_pen)
self.PTree = newclass.create("PTrees", SymbolTree, fitness=Fitness_)
# def produce
if initial_min is None:
initial_min = 2
self.register("genGrow", genGrow, pset=self.cpset, min_=initial_min, max_=initial_max + 1,
personal_map=self.personal_map)
self.register("genFull", genFull, pset=self.cpset, min_=initial_min, max_=initial_max + 1,
personal_map=self.personal_map)
self.register("genHalf", genGrow, pset=self.cpset, min_=initial_min, max_=initial_max + 1,
personal_map=self.personal_map)
self.register("gen_mu", genGrow, min_=1, max_=self.sub_mu_max + 1, personal_map=self.personal_map)
# def selection
self.register("select", selTournament, tournsize=2)
self.register("selKbestDim", selKbestDim,
dim_type=self.cpset.dim_type, fuzzy=self.cpset.fuzzy)
self.register("selBest", selBest)
self.register("mate", cxOnePoint)
# def mutate
self.register("mutate", mutUniform, expr=self.gen_mu, pset=self.cpset)
self.decorate("mate", staticLimit(key=operator.attrgetter(limit_type), max_value=self.max_value))
self.decorate("mutate", staticLimit(key=operator.attrgetter(limit_type), max_value=self.max_value))
if stats is None:
if cal_dim:
if score_pen[0] > 0:
stats = {"fitness_dim_max": ("max",), "dim_is_target": ("sum",)}
else:
stats = {"fitness_dim_min": ("min",), "dim_is_target": ("sum",)}
else:
if score_pen[0] > 0:
stats = {"fitness": ("max",)}
else:
stats = {"fitness": ("min",)}
self.stats = Statis_func(stats=stats)
logbook = Logbook()
logbook.header = ['gen'] + (self.stats.fields if self.stats else [])
self.logbook = logbook
if hall is None:
hall = 1
self.hall = HallOfFame(hall)
if re_hall is None:
self.re_hall = None
else:
if re_hall == 1 or re_hall == 0:
print("re_hall should more than 1")
re_hall = 2
assert re_hall >= hall, "re_hall should more than hall"
self.re_hall = HallOfFame(re_hall)
class BaseLoop(Toolbox):
"""
Base loop for BGP.
Examples::
if __name__ == "__main__":
pset = SymbolSet()
stop = lambda ind: ind.fitness.values[0] >= 0.880963
bl = BaseLoop(pset=pset, gen=10, pop=1000, hall=1, batch_size=40, re_hall=3, \n
n_jobs=12, mate_prob=0.9, max_value=5, initial_min=1, initial_max=2, \n
mutate_prob=0.8, tq=True, dim_type="coef", stop_condition=stop,\n
re_Tree=0, store=False, random_state=1, verbose=True,\n
stats={"fitness_dim_max": ["max"], "dim_is_target": ["sum"]},\n
add_coef=True, inter_add=True, inner_add=False, cal_dim=True, vector_add=False,\n
personal_map=False)
bl.run()
"""
def __init__(self, pset, pop=500, gen=20, mutate_prob=0.5, mate_prob=0.8, hall=1, re_hall=1,
re_Tree=None, initial_min=None, initial_max=3, max_value=5,
scoring=(r2_score,), score_pen=(1,), filter_warning=True, cv=1,
add_coef=True, inter_add=True, inner_add=False, vector_add=False, out_add=False, flat_add=False,
cal_dim=False, dim_type=None, fuzzy=False, n_jobs=1, batch_size=40,
random_state=None, stats=None, verbose=True, migrate_prob=0,
tq=True, store=False, personal_map=False, stop_condition=None, details=False, classification=False,
score_object="y", sub_mu_max=1, limit_type="h_bgp", batch_para=False):
"""
Parameters
----------
pset:SymbolSet
the feature x and target y and others should have been added.
pop: int
number of population.
gen: int
number of generation.
mutate_prob:float
probability of mutate.
mate_prob:float
probability of mate(crossover).
initial_max:int
max initial size of expression when first producing.
initial_min : None,int
min initial size of expression when first producing.
max_value:int
max size of expression.
limit_type: "height" or "length",","h_bgp"
limitation type for max_value, but don't affect initial_max, initial_min.
hall:int,>=1
number of HallOfFame (elite) to maintain.
re_hall:None or int>=2
Notes: only valid when hall
number of HallOfFame to add to next generation.
re_Tree: int
number of new features to add to next generation.
0 is false to add.
personal_map:bool or "auto"
"auto" is using 'premap' and with auto refresh the 'premap' with individual.\n
True is just using constant 'premap'.\n
False is just use the prob of terminals.
scoring: list of Callable, default is [sklearn.metrics.r2_score,]
See Also ``sklearn.metrics``
score_pen: tuple of 1, -1 or float but 0.
>0 : max problem, best is positive, worse -np.inf.
<0 : min problem, best is negative, worse np.inf.
Notes:
if multiply score method, the scores must be turn to same dimension in prepossessing
or weight by score_pen. Because the all the selection are stand on the mean(w_i*score_i)
Examples::
scoring = [r2_score,]
score_pen= [1,]
cv:sklearn.model_selection._split._BaseKFold,int
the shuffler must be False,
default=1 means no cv.
filter_warning:bool
filter warning or not.
add_coef:bool
add coef in expression or not.
inter_add：bool
add intercept constant or not.
inner_add:bool
add inner coefficients or not.
out_add:bool
add out coefficients or not.
flat_add:bool
add flat coefficients or not.
n_jobs:int
default 1, advise 6.
batch_size:int
default 40, depend of machine.
random_state:int
None,int.
cal_dim:bool
escape the dim calculation.
dim_type:Dim or None or list of Dim
"coef": af(x)+b. a,b have dimension,f(x)'s dimension is not dnan. \n
"integer": af(x)+b. f(x) is with integer dimension. \n
[Dim1,Dim2]: f(x)'s dimension in list. \n
Dim: f(x) ~= Dim. (see fuzzy) \n
Dim: f(x) == Dim. \n
None: f(x) == pset.y_dim
fuzzy:bool
choose the dim with same base with dim_type, such as m,m^2,m^3.
stats:dict
details of logbook to show. \n
Map:\n
values
= {"max": np.max, "mean": np.mean, "min": np.mean, "std": np.std, "sum": np.sum}
keys
= {\n
"fitness": just see fitness[0], \n
"fitness_dim_max": max problem, see fitness with demand dim,\n
"fitness_dim_min": min problem, see fitness with demand dim,\n
"dim_is_target": demand dim,\n
"coef": dim is True, coef have dim, \n
"integer": dim is integer, \n
...
}
if stats is None, default is:
for cal_dim=True:
stats = {"fitness_dim_max": ("max",), "dim_is_target": ("sum",)}
for cal_dim=False
stats = {"fitness": ("max",)}
if self-definition, the key is func to get attribute of each ind.
Examples::
def func(ind):
return ind.fitness[0]
stats = {func: ("mean",), "dim_is_target": ("sum",)}
verbose:bool
print verbose logbook or not.
tq:bool
print progress bar or not.
store:bool or path
bool or path.
stop_condition:callable
stop condition on the best ind of hall, which return bool,the true means stop loop.
Examples::
def func(ind):
c = ind.fitness.values[0]>=0.90
return c
details:bool
return expr and predict_y or not.
classification: bool
classification or not.
score_object:
score by y or delta y (for implicit function).
"""
super(BaseLoop, self).__init__()
assert initial_max <= max_value, "the initial size of expression should less than max_value limitation"
if cal_dim:
assert all(
[isinstance(i, Dim) for i in pset.dim_ter_con.values()]), \
"all import dim of pset should be Dim object."
self.details = details
self.max_value = max_value
self.pop = pop
self.gen = gen
self.mutate_prob = mutate_prob
self.mate_prob = mate_prob
self.migrate_prob = migrate_prob
self.verbose = verbose
self.cal_dim = cal_dim
self.re_hall = re_hall
self.re_Tree = re_Tree
self.store = store
self.limit_type = limit_type
self.data_all = []
self.personal_map = personal_map
self.stop_condition = stop_condition
self.population = []
self.rand_state = None
self.random_state = random_state
self.sub_mu_max = sub_mu_max
self.population_next = []
self.cpset = CalculatePrecisionSet(pset, scoring=scoring, score_pen=score_pen,
filter_warning=filter_warning, cal_dim=cal_dim,
add_coef=add_coef, inter_add=inter_add, inner_add=inner_add,
vector_add=vector_add, out_add=out_add, flat_add=flat_add, cv=cv,
n_jobs=n_jobs, batch_size=batch_size, tq=tq,
fuzzy=fuzzy, dim_type=dim_type, details=details,
classification=classification, score_object=score_object,
batch_para=batch_para
)
Fitness_ = newclass.create("Fitness_", Fitness, weights=score_pen)
self.PTree = newclass.create("PTrees", SymbolTree, fitness=Fitness_)
# def produce
if initial_min is None:
initial_min = 2
self.register("genGrow", genGrow, pset=self.cpset, min_=initial_min, max_=initial_max + 1,
personal_map=self.personal_map)
self.register("genFull", genFull, pset=self.cpset, min_=initial_min, max_=initial_max + 1,
personal_map=self.personal_map)
self.register("genHalf", genGrow, pset=self.cpset, min_=initial_min, max_=initial_max + 1,
personal_map=self.personal_map)
self.register("gen_mu", genGrow, min_=1, max_=self.sub_mu_max + 1, personal_map=self.personal_map)
# def selection
self.register("select", selTournament, tournsize=2)
self.register("selKbestDim", selKbestDim,
dim_type=self.cpset.dim_type, fuzzy=self.cpset.fuzzy)
self.register("selBest", selBest)
self.register("mate", cxOnePoint)
# def mutate
self.register("mutate", mutUniform, expr=self.gen_mu, pset=self.cpset)
self.decorate("mate", staticLimit(key=operator.attrgetter(limit_type), max_value=self.max_value))
self.decorate("mutate", staticLimit(key=operator.attrgetter(limit_type), max_value=self.max_value))
if stats is None:
if cal_dim:
if score_pen[0] > 0:
stats = {"fitness_dim_max": ("max",), "dim_is_target": ("sum",)}
else:
stats = {"fitness_dim_min": ("min",), "dim_is_target": ("sum",)}
else:
if score_pen[0] > 0:
stats = {"fitness": ("max",)}
else:
stats = {"fitness": ("min",)}
self.stats = Statis_func(stats=stats)
logbook = Logbook()
logbook.header = ['gen'] + (self.stats.fields if self.stats else [])
self.logbook = logbook
if hall is None:
hall = 1
self.hall = HallOfFame(hall)
if re_hall is None:
self.re_hall = None
else:
if re_hall == 1 or re_hall == 0:
print("re_hall should more than 1")
re_hall = 2
assert re_hall >= hall, "re_hall should more than hall"
self.re_hall = HallOfFame(re_hall)
def varAnd(self, *arg, **kwargs):
return varAnd(*arg, **kwargs)
def to_csv(self, data_all):
"""store to csv"""
if self.store:
if isinstance(self.store, str):
path = self.store
else:
path = os.getcwd()
file_new_name = "_".join((str(self.pop), str(self.gen),
str(self.mutate_prob), str(self.mate_prob),
str(time.time())))
try:
st = Store(path)
st.to_csv(data_all, file_new_name, transposition=True)
print("store data to ", path, file_new_name)
except (IOError, PermissionError):
st = Store(os.getcwd())
st.to_csv(data_all, file_new_name, transposition=True)
print("store data to ", os.getcwd(), file_new_name)
def maintain_halls(self, population):
"""maintain the best p expression"""
if self.re_hall is not None:
maxsize = max(self.hall.maxsize, self.re_hall.maxsize)
if self.cal_dim:
inds_dim = self.selKbestDim(population, maxsize)
else:
inds_dim = self.selBest(population, maxsize)
self.hall.update(inds_dim)
self.re_hall.update(inds_dim)
sole_inds = [i for i in self.re_hall.items if i not in inds_dim]
inds_dim.extend(sole_inds)
else:
if self.cal_dim:
inds_dim = self.selKbestDim(population, self.hall.maxsize)
else:
inds_dim = self.selBest(population, self.hall.maxsize)
self.hall.update(inds_dim)
inds_dim = []
inds_dim = copy.deepcopy(inds_dim)
return inds_dim
def re_add(self):
"""add the expression as a feature"""
if self.hall.items and self.re_Tree:
it = self.hall.items
indo = it[random.choice(len(it))]
ind = copy.deepcopy(indo)
inds = ind.depart()
if not inds:
pass
else:
inds = [self.cpset.calculate_detail(indi) for indi in inds]
le = min(self.re_Tree, len(inds))
indi = inds[random.choice(le)]
self.cpset.add_tree_to_features(indi)
def re_fresh_by_name(self, *arr):
re_name = ["mutate", "genGrow", "genFull", "genHalf"]
if len(arr) > 0:
re_name.extend(arr)
self.refresh(re_name, pset=self.cpset)
# for i in re_name + ["mate"]: # don‘t del this
# self.decorate(i, staticLimit(key=operator.attrgetter("height"), max_value=2 * (self.max_value + 1)))
def top_n(self, n=10, gen=-1, key="value", filter_dim=True, ascending=False):
"""
Return the best n results.
Note:
Only valid in ``store=True``.
Parameters
----------
n:int
n.
gen:
the generation, default is -1.
key: str
sort keys, default is "values".
filter_dim:
filter no-dim expressions or not.
ascending:
reverse.
Returns
-------
top n results.
pd.DataFrame
"""
import pandas as pd
if self.store == "False":
raise TypeError("Only valid with store=True")
data = self.data_all
data = pd.DataFrame(data)
if gen == -1:
gen = max(data["gen"])
data = data[data["gen"] == gen]
if filter_dim:
data = data[data["dim_score"] == 1]
data = data.drop_duplicates(['expr'], keep="first")
if key is not None:
data[key] = data[key].str.replace("(", "")
data[key] = data[key].str.replace(")", "")
data[key] = data[key].str.replace(",", "")
try:
data[key] = data[key].astype(float)
except ValueError:
raise TypeError("check this key column can be translated into float")
data = data.sort_values(by='value', ascending=ascending).iloc[:n, :]
return data
def check_height_length(self, pop, site=""):
old = len(pop)
if self.limit_type == 'height':
pop = [i for i in pop if i.height <= self.max_value]
elif self.limit_type == 'length':
pop = [i for i in pop if i.length <= self.max_value]
else:
pop = [i for i in pop if i.h_bgp <= self.max_value]
new = len(pop)
if old == new:
pass
else:
if site != "":
print(site)
# raise TypeError
index = random.randint(0, new, old - new)
pop.extend([pop[i] for i in index])
return pop
def run(self, warm_start=False, new_gen=None):
"""
Parameters
----------
warm_start:bool
warm_start from last result.
new_gen:
new generations for warm_startm, default is the initial generations.
"""
# 1.generate###################################################################
if warm_start is False:
random.seed(self.random_state)
population = [self.PTree(self.genHalf()) for _ in range(self.pop)]
gen_i = 0
gen = self.gen
else:
assert self.population_next != []
random.set_state(self.rand_state)
population = self.population_next
gen_i = self.gen_i
self.re_fresh_by_name()
if new_gen:
gen = gen_i + new_gen
else:
gen = gen_i + self.gen
for gen_i in range(gen_i + 1, gen + 1):
population_old = copy.deepcopy(population)
# 2.evaluate###############################################################
invalid_ind_score = self.cpset.parallelize_score(population_old)
for ind, score in zip(population_old, invalid_ind_score):
ind.fitness.values = tuple(score[0])
ind.y_dim = score[1]
ind.dim_score = score[2]
ind.coef_expr = score[3]
ind.coef_pre_y = score[4]
population = population_old
# 3.log###################################################################
# 3.1.log-print##############################
record = self.stats.compile(population) if self.stats else {}
self.logbook.record(gen=gen_i, **record)
if self.verbose:
print(self.logbook.stream)
# 3.2.log-store##############################
if self.store:
datas = [{"gen": gen_i, "name": str(pop_i), "expr": str([pop_i.coef_expr]),
"value": str(pop_i.fitness.values),
"dimension": str(pop_i.y_dim),
"dim_score": pop_i.dim_score} for pop_i in population]
self.data_all.extend(datas)
self.population = copy.deepcopy(population)
# 3.3.log-hall###############################
inds_dim = self.maintain_halls(population)
# 4.refresh################################################################
# 4.1.re_update the premap ##################
if self.personal_map == "auto":
[self.cpset.premap.update(indi, self.cpset) for indi in inds_dim]
# 4.2.re_add_tree and refresh pset###########
if self.re_Tree:
self.re_add()
self.re_fresh_by_name()
# 6.next generation ！！！！#######################################################
# selection and mutate,mate,migration
population = self.select(population, int((1 - self.migrate_prob) * len(population)) - len(inds_dim))
offspring = self.varAnd(population, self, self.mate_prob, self.mutate_prob)
offspring.extend(inds_dim)
migrate_pop = [self.PTree(self.genFull()) for _ in range(int(self.migrate_prob * len(population)))]
population[:] = offspring + migrate_pop
population = self.check_height_length(population)
# 5.break#######################################################
if self.stop_condition is not None:
if self.stop_condition(self.hall.items[0]):
break
# 7 freeze ###################################################
self.rand_state = random.get_state()
self.population_next = population
self.gen_i = gen_i
# final.store#####################################################################
if self.store:
self.to_csv(self.data_all)
self.hall.items = [self.cpset.calculate_detail(indi) for indi in self.hall.items]
return self.hall
sl = [SymbolTree.genGrow(pset0, 3, 4) for _ in range(500)]
a = time.time()
# sli =" MAdd(Sub(Add(Mul(x0, gx1), exp(x10)), Mul(Conv(Add(x0, gx0)), Mul(x6, MAdd(x10)))))"
# sl =["MAdd(gx1 * x11 * (-x0 + x11) * MAdd(gx1))"]
# sl = [compile_context(sli, pset0.context, pset0.gro_ter_con) for sli in sl]
# sl = [simple(sli.args[0], pset0.gro_ter_con) for sli in sl if len(sli.args)>0]
c = 1
a = time.time()
pset0 = CalculatePrecisionSet(pset0,
scoring=None,
score_pen=(1, ),
filter_warning=True,
cal_dim=False,
dim_type=None,
fuzzy=False,
add_coef=False,
inter_add=True,
inner_add=False,
n_jobs=1,
batch_size=20,
tq=True)
from sys import getsizeof
T100 = getsizeof(sl)
psize = getsizeof(pset0)
# print(T100,psize)
for i in sl:
b = time.time()
i0 = compile_context(i, pset0.context, pset0.gro_ter_con)
r2 = time.time()
x[:, 2] = x[:, 0] / x[:, 1]
y = np.zeros(x.shape[0])
x = x
y = y
pset = SymbolSet()
pset.add_features(x, y)
pset.add_operations(
categories=("Add", "Mul", "Self", "Abs"),
self_categories=None)
from sklearn.metrics import r2_score, mean_squared_error
cp = CalculatePrecisionSet(pset, scoring=[r2_score, mean_squared_error],
score_pen=[1, -1],
filter_warning=True)
x0, x1, x2 = sympy.symbols("x0, x1, x2")
# t=Function("t")
# expr00 = (x2*x1+x0*x2*2).subs(x0, t(x1))
# dv1 = sympy.diff(expr00, x1, evaluate=True)
# dv1 = dv1.subs(t(x1), x0)
#
# t = Function("t")
# expr00 = (x2*x1+x0*x2*2).subs(x1, t(x0))
# dv2 = sympy.diff(expr00, x0, evaluate=True)
# dv2 = dv2.subs(t(x0), x1)
#
# k = dv1/dv2
#
def test_pset_passed_to_cpset_will_change(self):
cp = CalculatePrecisionSet(self.pset)
self.assertNotEqual(cp, self.cp)
class MyTestbase(unittest.TestCase):
def setUp(self):
self.SymbolTree = SymbolTree
self.pset = SymbolSet()
from sklearn.datasets import fetch_california_housing
data = fetch_california_housing()
x = data["data"][:100]
y = data["target"][:100]
# 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 test_pset_passed_to_cpset_will_change(self):
cp = CalculatePrecisionSet(self.pset)
self.assertNotEqual(cp, self.cp)
def test_tree_gengrow_repr_and_str_different(self):
from numpy import random
random.seed(1)
sl = SymbolTree.genGrow(self.pset, 3, 4)
print(sl)
# self.assertNotEqual(repr(sl), str(sl))
def test_add_tree_back(self):
from numpy import random
random.seed(1)
sl = SymbolTree.genGrow(self.pset, 3, 4)
self.pset.add_tree_to_features(sl)
#
def test_barch_tree(self):
from numpy import random
random.seed(1)
for i in range(10):
sl = SymbolTree.genGrow(self.pset, 3, 4)
cpsl = self.cp.calculate_detail(sl)
self.assertIsNotNone(cpsl.y_dim)
self.assertIsNotNone(cpsl.expr)
self.assertIsNone(cpsl.p_name)
if cpsl.pre_y is not None:
self.assertIsInstance(cpsl.pre_y, numpy.ndarray)
self.assertEqual(cpsl.pre_y.shape, self.y.shape)
print(cpsl.coef_pre_y[:3])
print(cpsl.pre_y[:3])
print(cpsl.coef_score)
print(cpsl.coef_expr)
print(cpsl.pure_expr)
def test_depart_tree(self):
from numpy import random
random.seed(1)
for i in range(10):
sl = SymbolTree.genGrow(self.pset, 5, 6)
sl_departs = sl.depart()
for i in sl_departs:
cpsl = self.cp.calculate_simple(i)
self.assertIsNotNone(cpsl.y_dim)
self.assertIsNotNone(cpsl.expr)
self.assertIsNone(cpsl.p_name)
