from os import path
sys.path.insert(0, "../../ISANet")
sys.path.insert(0, "./")
from isanet.model_selection import Kfold
from isanet.utils.model_utils import save_data
import numpy as np
np.random.seed(42)
TS = np.genfromtxt('ML-CUP19-TR.csv', delimiter=',')
np.random.shuffle(TS)
np.savetxt('cup20/ML-CUP19-TR_tr_vl.csv', TS[:1500, 1:], delimiter=',')
np.savetxt('cup20/ML-CUP19-TR_test.csv', TS[1500:, 1:], delimiter=',')
kf = Kfold(n_splits=4, shuffle=True)
split = kf.split(TS[:1500, 1:])
save_data(split, "cup20/4folds.index")
ten_features = [1, 2, 3, 4, 5, 6, 7, 8, 12, 15, 21, 22]
np.savetxt('cup10/ML-CUP19-TR_tr_vl_10.csv',
TS[:1500, ten_features],
delimiter=',')
np.savetxt('cup10/ML-CUP19-TR_test_10.csv',
TS[1500:, ten_features],
delimiter=',')
save_data(split, "cup10/4folds.index")
from isanet.optimizer import SGD
from isanet.utils.model_utils import printMSE, printAcc, plotMse, save_data, load_data
from isanet.optimizer import EarlyStopping
from isanet.model_selection import Kfold, GridSearchCV
import numpy as np
dataset = np.genfromtxt('../dataset/cup10/ML-CUP19-TR_tr_vl_10.csv',
delimiter=',')
split = load_data("../dataset/cup10/4folds.index")
X_train = dataset[:, :-2]
Y_train = dataset[:, -2:]
es = EarlyStopping(0.009, 200)
mlp_r = MLPRegressor(X_train.shape[1], Y_train.shape[1])
grid = {
"n_layer_units": [[80], [100]],
"learning_rate": [0.03, 0.06, 0.08, 0.098],
"max_epoch": [30000],
"momentum": [0.2, 0.4, 0.6, 0.8, 0.9],
"nesterov": [True],
"kernel_regularizer": [0.0001, 0.0005, 0.0009, 0.0013],
"activation": ["sigmoid"],
"early_stop": [es],
}
gs = GridSearchCV(estimator=mlp_r, param_grid=grid, cv=split, verbose=1)
result = gs.fit(X_train, Y_train)
save_data(result, 'large_grid_2_result.data')
from isanet.model_selection import Kfold, GridSearchCV
import numpy as np
dataset = np.genfromtxt('../dataset/cup10/ML-CUP19-TR_tr_vl_10.csv',
delimiter=',')
split = load_data("../dataset/cup10/4folds.index")
X_train = dataset[:, :-2]
Y_train = dataset[:, -2:]
es = EarlyStopping(0.009, 200)
mlp_r = MLPRegressor(X_train.shape[1], Y_train.shape[1])
grid = {
"n_layer_units": [[70], [80]],
# "learning_rate": [0.03, 0.36, 0.042, 0.048, 0.54, 0.06],
"learning_rate":
[0.03, 0.034, 0.038, 0.042, 0.046, 0.050, 0.054, 0.058, 0.06],
"max_epoch": [30000],
"momentum": [0.6, 0.7, 0.8, 0.9],
"nesterov": [True],
"kernel_regularizer": [0.0001, 0.0002, 0.0003, 0.0004, 0.0005],
"activation": ["sigmoid"],
"early_stop": [es],
}
gs = GridSearchCV(estimator=mlp_r, param_grid=grid, cv=split, verbose=1)
result = gs.fit(X_train, Y_train)
save_data(result, 'finer_grid_2_result.data')