def test_29_load(self):
global auto_lmgpip_regressor, auto_lmgpip_basic_regressor
auto_lmgpip_regressor = AutoAdvanced(
**auto_lmgpip_regressor_params).load()
auto_lmgpip_basic_regressor = AutoBasic(
**auto_lmgpip_regressor_params).load()
self.assertIsInstance(auto_lmgpip_regressor, AutoAdvanced,
"Load failed")
self.assertIsInstance(auto_lmgpip_basic_regressor, AutoBasic,
"Load failed")
def test_23_load(self):
global auto_lmgpip_nn, auto_lmgpip_basic_nn
auto_lmgpip_nn = AutoAdvanced(**auto_lmgpip_params).load()
auto_lmgpip_basic_nn = AutoBasic(**auto_lmgpip_params).load()
self.assertIsInstance(auto_lmgpip_nn, AutoAdvanced, "Load failed")
self.assertIsInstance(auto_lmgpip_basic_nn, AutoBasic, "Load failed")
def test_11_load(self):
global auto_mushroom_nn, auto_mushroom_basic_nn
auto_mushroom_nn = AutoAdvanced(**auto_mushroom_params).load()
auto_mushroom_basic_nn = AutoBasic(**auto_mushroom_params).load()
self.assertIsInstance(auto_mushroom_nn, AutoAdvanced, "Load failed")
self.assertIsInstance(auto_mushroom_basic_nn, AutoBasic, "Load failed")
def test_04_load(self):
global nn, basic_nn
nn = AutoAdvanced(**base_params).load()
basic_nn = AutoBasic(**base_params).load()
self.assertIsInstance(nn, AutoAdvanced, "Load failed")
self.assertIsInstance(basic_nn, AutoBasic, "Load failed")
import numpy as np
from Util.Util import DataUtil
from _Dist.NeuralNetworks.b_TraditionalML.SVM import AutoLinearSVM
from _Dist.NeuralNetworks.f_AutoNN.NN import AutoBasic, AutoAdvanced
from _Dist.NeuralNetworks._Tests._UnitTests.UnitTestUtil import clear_cache
base_params = {
"name": "UnitTest",
"data_info": {},
"model_param_settings": {
"n_epoch": 1,
"max_epoch": 2
}
}
nn = AutoAdvanced(**copy.deepcopy(base_params))
basic_nn = AutoBasic(**copy.deepcopy(base_params))
linear_svm = AutoLinearSVM(**copy.deepcopy(base_params))
train_set, cv_set, test_set = DataUtil.gen_special_linear(1000,
2,
2,
2,
one_hot=False)
auto_mushroom_params = copy.deepcopy(base_params)
auto_mushroom_params["name"] = "mushroom"
auto_mushroom_params["data_info"]["file_type"] = "txt"
auto_mushroom_params["data_info"]["data_folder"] = "../_Data"
mushroom_labels = {"p", "e"}
mushroom_file = "../_Data/mushroom"
auto_mushroom_nn = AutoAdvanced(**copy.deepcopy(auto_mushroom_params))
import os
import sys
root_path = os.path.abspath("../../../../")
if root_path not in sys.path:
sys.path.append(root_path)
from _Dist.NeuralNetworks.f_AutoNN.NN import AutoAdvanced
AutoAdvanced("Adult",
model_param_settings={
"n_epoch": 3
},
data_info={
"file_type": "csv",
"data_folder": "../_Data"
}).fit(snapshot_ratio=0).fit(snapshot_ratio=0).save()
AutoAdvanced("Adult").load().fit(snapshot_ratio=0)
import os
import sys
root_path = os.path.abspath("../../../../")
if root_path not in sys.path:
sys.path.append(root_path)
from _Dist.NeuralNetworks._Tests.TestUtil import draw_acc
from _Dist.NeuralNetworks.f_AutoNN.NN import AutoBasic, AutoAdvanced
from _Dist.NeuralNetworks._Tests.Madelon.MadelonUtil import get_madelon
x, y, x_test, y_test = get_madelon()
base_params = {
"name": "Madelon",
"model_param_settings": {"n_epoch": 200, "metric": "acc"},
"model_structure_settings": {"hidden_units": [152, 153]}
}
basic = AutoBasic(**base_params).fit(x, y, x_test, y_test, snapshot_ratio=0)
advanced = AutoAdvanced(**base_params).fit(x, y, x_test, y_test, snapshot_ratio=0)
draw_acc(basic, advanced, ylim=(0.5, 1.05))