Parameters details¶
For both fastgplearn.skflow.SymbolicRegressor and fastgplearn.skflow.SymbolicClassifier .
Init Parameters:¶
Parameters name |
Type |
Default |
Suggest Range |
Definition |
population_size |
(int) |
10000 |
[50, 1000000] |
number of population |
generations |
(int) |
20 |
[10,…] |
number of generations |
tournament_size |
(int) |
20 |
[5,20] |
number of for each turn of tournament |
stopping_criteria |
(float) |
0.95 |
[0,1] |
early stopping criteria |
constant_range |
tuple of float |
(0,1.0) |
/ |
constants would choice from range randomly |
constants |
tuple of float |
None |
/ |
if given, the parameter constant_range would be ignored, and just use the constants offered |
depth |
tuple of float |
(2,5) |
1st [1,…], 2ed [2,8) |
(min_depth,max_depth), keep the max of depth is not more than 8 ! |
function_set |
tuple of string |
(+-*/) |
/ |
optional: (‘add’, ‘sub’, ‘mul’, ‘div’, “ln”, “exp”, “pow2”, “pow3”, “rec”, “max”, “min”, “sin”, “cos”) |
n_jobs |
(int) |
1 |
[1,…] |
n jobs to parallel |
verbose |
(bool) |
True |
True,False |
print message |
p_mutate |
(float) |
0.5 |
(0,1) |
mutate probability |
p_crossover |
(float) |
0.5 |
(0,1) |
crossover probability |
random_state |
(int) |
None |
/ |
random state |
hall_of_fame |
(int) |
3 |
[0,10] |
hall of frame number to add to next generation |
store_of_fame |
(int) |
3 |
[0,10] |
hall of frame number to return result |
method_backend |
(string) |
“p_numpy” |
/ |
optional: (“p_numpy”,”c_numpy”,”p_torch”,”c_torch”) |
device |
(string) |
“cpu” |
/ |
optional: (“cpu”,”cuda:0”, …) depend on your computer device |
func_p |
(np.ndarray) |
None |
/ |
specific the probability values of each function |
sci_template |
list, str |
“default” |
/ |
user self-defined list template or “default” or None |
Fit Parameters:¶
Fit parameters in SymbolicRegressor().fit() or SymbolicClassifier().fit() method.
Parameters name |
Type |
Default |
Suggest Range |
Definition |
X |
(np.ndarray) |
/ |
/ |
with shape (n_sample, n_fea) |
y |
(np.ndarray) |
/ |
/ |
with shape (n_sample,) |
xs_p |
(np.ndarray) |
None |
/ |
specific the probability values of each feature |
x_label |
list of string |
None |
/ |
specific the name values of each feature |
Other Parameters:¶
Other parameters present in predict() or score(), or top_n() method.
Parameters name |
Type |
Default |
Suggest Range |
Definition |
X |
(np.ndarray) |
/ |
/ |
with shape (n_sample, n_fea) |
y |
(np.ndarray) |
/ |
/ |
with shape (n_sample,) |
n |
(int) |
0 |
0 |
specify the number of expression to calculate or score |
scoring |
(str) |
/ |
/ |
for regression, default is “r2”, for classification is “accuracy” |