advanced_params["model_structure_settings"]["use_dndf"] = True
wnd_dndf = Advanced(**advanced_params).fit(x, y, x_test, y_test, snapshot_ratio=0)
advanced_params["model_structure_settings"]["use_pruner"] = True
wnd_dndf_pruned = Advanced(**advanced_params).fit(x, y, x_test, y_test, snapshot_ratio=0)
print("BasicNN ", end="")
basic.evaluate(x, y, x_cv, y_cv, x_test, y_test)
print("WnD ", end="")
wnd.evaluate(x, y, x_cv, y_cv, x_test, y_test)
print("WnD & DNDF ", end="")
wnd_dndf.evaluate(x, y, x_cv, y_cv, x_test, y_test)
print("WnD & DNDF & Pruner ", end="")
wnd_dndf_pruned.evaluate(x, y, x_cv, y_cv, x_test, y_test)
draw_acc(basic, wnd_dndf, wnd_dndf_pruned)
advanced_params["data_info"]["numerical_idx"] = [True] * 500 + [False]
advanced_params["model_structure_settings"]["pruner_params"] = {"prune_method": "hard_prune"}
basic, wnd_dndf, wnd_dndf_pruned = block_test(DataUtil.gen_noisy_linear, n_dim=500)
draw_acc(basic, wnd_dndf, wnd_dndf_pruned, ylim=(0.8, 0.95), draw_train=False)
basic, wnd_dndf, wnd_dndf_pruned = block_test(DataUtil.gen_noisy_linear, n_dim=500, noise_scale=1.)
draw_acc(basic, wnd_dndf, wnd_dndf_pruned, ylim=(0.7, 0.9), draw_train=False)
advanced_params["data_info"]["numerical_idx"] = [True] * 100 + [False]
basic, wnd_dndf, wnd_dndf_pruned = block_test(DataUtil.gen_noisy_linear, n_valid=100, noise_scale=0.)
draw_acc(basic, wnd_dndf, wnd_dndf_pruned, ylim=(0.95, 1.), draw_train=False)
advanced_params["model_structure_settings"]["pruner_params"] = {
"prune_method": "soft_prune",
(x, y), (x_test, y_test) = DataUtil.gen_noisy_linear(n_dim=2,
n_valid=2,
test_ratio=0.01,
one_hot=False)
svm = LinearSVM(**deepcopy(base_params)).fit(x,
y,
x_test,
y_test,
snapshot_ratio=0).visualize2d(
x_test, y_test)
nn = AutoAdvanced("NoisyLinear",
**deepcopy(base_params)).fit(x,
y,
x_test,
y_test,
snapshot_ratio=0).visualize2d(
x_test, y_test)
draw_acc(svm, nn)
base_params["data_info"] = {"data_folder": "../_Data"}
svm = AutoLinearSVM("mushroom", **deepcopy(base_params)).fit(snapshot_ratio=0)
nn = AutoAdvanced("mushroom", **deepcopy(base_params)).fit(snapshot_ratio=0)
draw_acc(svm, nn)
base_params["data_info"]["file_type"] = "csv"
svm = AutoLinearSVM("Adult", **deepcopy(base_params)).fit(snapshot_ratio=0)
nn = AutoAdvanced("Adult", **deepcopy(base_params)).fit(snapshot_ratio=0)
draw_acc(svm, nn)
DistLinearSVM("Adult", **deepcopy(base_params)).k_random()
import os
import sys
root_path = os.path.abspath("../../../../")
if root_path not in sys.path:
sys.path.append(root_path)
from copy import deepcopy
from Util.Util import DataUtil
from _Dist.NeuralNetworks._Tests.TestUtil import draw_acc
from _Dist.NeuralNetworks.b_TraditionalML.SVM import SVM
from _Dist.NeuralNetworks.f_AutoNN.DistNN import AutoAdvanced
base_params = {"model_param_settings": {"n_epoch": 30, "metric": "acc"}}
(x, y), (x_test, y_test) = DataUtil.gen_noisy_linear(n_dim=2, n_valid=2, test_ratio=0.01, one_hot=False)
svm = SVM(**deepcopy(base_params)).fit(
x, y, x_test, y_test, snapshot_ratio=0).visualize2d(x_test, y_test)
nn = AutoAdvanced("NoisyLinear", **deepcopy(base_params)).fit(
x, y, x_test, y_test, snapshot_ratio=0).visualize2d(x_test, y_test)
draw_acc(svm, nn)
base_params = {
"model_param_settings": {"n_epoch": 200, "metric": "acc"},
"model_structure_settings": {"hidden_units": [152, 153]}
}
basic = Basic(**base_params).fit(x, y, x_test, y_test, snapshot_ratio=0)
advanced_params = {"data_info": {
"numerical_idx": [True] * 500 + [False], "categorical_columns": []
}}
advanced_params.update(base_params)
advanced_params["model_param_settings"]["keep_prob"] = 0.5
advanced_params["model_param_settings"]["use_batch_norm"] = False
advanced_params["model_structure_settings"]["use_pruner"] = False
advanced_params["model_structure_settings"]["use_wide_network"] = False
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)
advanced_params["model_param_settings"]["keep_prob"] = 0.25
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)
advanced_params["model_param_settings"]["keep_prob"] = 0.1
advanced_params["model_param_settings"]["n_epoch"] = 600
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)
advanced_params["model_param_settings"]["n_epoch"] = 200
advanced_params["model_param_settings"]["keep_prob"] = 0.25
advanced_params["model_param_settings"]["use_batch_norm"] = True
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)
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))
}
basic = Basic(**base_params).fit(x, y, x_test, y_test, snapshot_ratio=0)
advanced_params = {
"data_info": {
"numerical_idx": [True] * 500 + [False],
"categorical_columns": []
}
}
advanced_params.update(base_params)
advanced_params["model_param_settings"]["keep_prob"] = 0.5
advanced_params["model_param_settings"]["use_batch_norm"] = False
advanced_params["model_structure_settings"]["use_pruner"] = False
advanced_params["model_structure_settings"]["use_wide_network"] = False
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)
advanced_params["model_param_settings"]["keep_prob"] = 0.25
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)
advanced_params["model_param_settings"]["keep_prob"] = 0.1
advanced_params["model_param_settings"]["n_epoch"] = 600
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)
advanced_params["model_param_settings"]["n_epoch"] = 200
advanced_params["model_param_settings"]["keep_prob"] = 0.25
advanced_params["model_param_settings"]["use_batch_norm"] = True
advanced = block_evaluate()
draw_acc(basic, advanced, ylim=ylim)