-
Notifications
You must be signed in to change notification settings - Fork 0
/
cv_experiment.py
315 lines (242 loc) · 15.9 KB
/
cv_experiment.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
import os
import datetime
import wandb
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from models.mlp import initialise_mlp_models
from models.lenet import initialise_lenet_models
from models.wrn import initialise_resnet32_modellist
from utils.kernel import RBF
from utils.data import get_sine_data, get_gap_data, load_yacht_data, \
load_energy_data, load_autompg_data, load_concrete_data, load_kin8nm_data, \
load_protein_data, load_naval_data, load_power_data, load_parkinson_data, \
load_mnist_data, load_fashionmnist_data, load_breast_data, load_heart_data, \
load_ionosphere_data, load_australian_data, load_cifar10_data, load_wine_data
from utils.plot import plot_modellist
from utils.eval import regression_evaluate_modellist, classification_evaluate_modellist
from train.train import train
from utils.experiment_utils import Dataset, generate_model_path
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def run_experiment(cfg):
# Load your dataset here
print(cfg.task.dataset)
if cfg.task.dataset == "sine":
x_train, y_train, x_test, y_test = get_sine_data(n_samples=cfg.experiment.n_samples, seed= cfg.experiment.seed)
elif cfg.task.dataset == "gap":
x_train, y_train, x_test, y_test = get_gap_data(n_samples=cfg.experiment.n_samples, seed= cfg.experiment.seed)
elif cfg.task.dataset == "yacht":
x_train, y_train, x_test, y_test = load_yacht_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset == "energy":
x_train, y_train, x_test, y_test = load_energy_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset == "autompg":
x_train, y_train, x_test, y_test = load_autompg_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="concrete":
x_train, y_train, x_test, y_test = load_concrete_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="kin8nm":
x_train, y_train, x_test, y_test = load_kin8nm_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="naval":
x_train, y_train, x_test, y_test = load_naval_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="protein":
x_train, y_train, x_test, y_test = load_protein_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="power":
x_train, y_train, x_test, y_test = load_power_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="wine":
x_train, y_train, x_test, y_test = load_wine_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="parkinsons":
x_train, y_train, x_test, y_test = load_parkinson_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="breast":
x_train, y_train, x_test, y_test = load_breast_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="heart":
x_train, y_train, x_test, y_test = load_heart_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="ionosphere":
x_train, y_train, x_test, y_test = load_ionosphere_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="australian":
x_train, y_train, x_test, y_test = load_australian_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="mnist":
x_train, y_train, x_test, y_test = load_mnist_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="fashionmnist":
x_train, y_train, x_test, y_test = load_fashionmnist_data(test_size_split=cfg.experiment.train_val_split, seed=cfg.experiment.seed, config=cfg)
elif cfg.task.dataset =="cifar10":
x_train, y_train, x_test, y_test = load_cifar10_data(config=cfg)
else:
print('The configured dataset is not yet implemented')
ValueError("The configured dataset is not yet implemented")
return
n_splits = cfg.experiment.n_splits # Number of folds for k-fold CV
kf = KFold(n_splits=n_splits, shuffle=True, random_state=cfg.experiment.seed)
x_combined = np.concatenate((x_train, x_test), axis=0)
y_combined = np.concatenate((y_train, y_test), axis=0)
if cfg.task.task_type == 'regression':
n_metrics = 3 # metrics to track across folds
elif cfg.task.task_type == 'classification':
n_metrics = 7 # metrics to track across folds
metrics_array = np.zeros((n_splits, n_metrics)) # Store metrics from each fold
model_path = generate_model_path(cfg)
for fold, (train_idx, test_idx) in enumerate(kf.split(x_combined)):
print(f"Running fold {fold + 1}/{n_splits}")
method = cfg.experiment.method
task = cfg.task.dataset
#wandb_group = cfg.experiment.wandb_group
if method == 'SVN':
active_tags = [f"Fold_{fold+1}", method, task, cfg.SVN.hessian_calc]
else:
active_tags = [method, f"Fold_{fold+1}", task]
wandb_group = cfg.experiment.wandb_group
if cfg.experiment.wandb_logging:
wandb.init( project="SVN_Ensembles",
tags=active_tags,
entity="klemens-floege",
group=wandb_group
)
# Setting the configuration in WandB
if cfg.experiment.method == 'SVN':
wandb.config.update({
"learning_rate": cfg.experiment.lr,
"num_epochs": cfg.experiment.num_epochs,
"batch_size": cfg.experiment.batch_size,
"early_stopping": cfg.experiment.early_stopping,
"dataset": cfg.task.dataset,
"method": cfg.experiment.method,
"task_type": cfg.task.task_type,
"n_splits": cfg.experiment.n_splits,
"hessian_calc": cfg.SVN.hessian_calc,
"use_curvature_kernel": cfg.SVN.use_curvature_kernel
})
else:
wandb.config.update({
"learning_rate": cfg.experiment.lr,
"num_epochs": cfg.experiment.num_epochs,
"batch_size": cfg.experiment.batch_size,
"early_stopping": cfg.experiment.early_stopping,
"dataset": cfg.task.dataset,
"method": cfg.experiment.method,
"task_type": cfg.task.task_type,
"n_splits": cfg.experiment.n_splits
})
# Split data into training and validation for this fold
x_train_fold, x_test_fold = x_combined[train_idx], x_combined[test_idx]
y_train_fold, y_test_fold = y_combined[train_idx], y_combined[test_idx]
#initialise models and normalise data
output_dim = cfg.task.dim_problem # Assuming y_train is a vector; if it's a 2D array with one column, this is correct
if cfg.task.dataset in ['mnist', 'fashionmnist']:
image_dim = cfg.task.image_dim
modellist = initialise_lenet_models(image_dim, output_dim, cfg)
elif cfg.task.dataset in ['cifar10']:
modellist = initialise_resnet32_modellist(depth =34, widen_factor=1 , config=cfg)
else:
scaler = StandardScaler()
x_train_fold = scaler.fit_transform(x_train_fold)
x_test_fold = scaler.transform(x_test_fold)
input_dim = x_train.shape[1] # Number of features in x_train
modellist = initialise_mlp_models(input_dim, output_dim, cfg)
# Split the training data into training and evaluation sets
x_train_split, x_eval_split, y_train_split, y_eval_split = train_test_split(x_train_fold, y_train_fold, test_size=cfg.experiment.train_val_split, random_state=cfg.experiment.seed)
# Create instances of the SineDataset for each set
train_dataset = Dataset(x_train_split, y_train_split)
eval_dataset = Dataset(x_eval_split, y_eval_split)
test_dataset = Dataset(x_test_fold, y_test_fold)
print("length train", len(train_dataset))
print("length eval", len(eval_dataset))
print("length test", len(test_dataset))
#create dataloaders
train_dataloader = DataLoader(train_dataset, batch_size=cfg.experiment.batch_size, shuffle=cfg.experiment.shuffle)
eval_dataloader = DataLoader(eval_dataset, batch_size=cfg.experiment.batch_size, shuffle=cfg.experiment.shuffle)
test_dataloader = DataLoader(test_dataset, batch_size=cfg.experiment.batch_size, shuffle=cfg.experiment.shuffle)
# Train and evaluate as before
avg_train_time_per_epoch = train(modellist, cfg.experiment.lr, cfg.experiment.num_epochs, train_dataloader, eval_dataloader, device, cfg)
if cfg.task.task_type == 'regression':
test_MSE, test_rmse, test_nll = regression_evaluate_modellist(modellist, dataloader=test_dataloader, device=device, config=cfg)
print(f"Test MSE: {test_MSE:.4f}, Test RMSE: {test_rmse:.4f}, Test NLL: {test_nll:.4f}, Avg Time / Epoch: {avg_train_time_per_epoch:.4f} ")
# Log regression test metrics
if cfg.experiment.wandb_logging:
wandb.run.summary.update({
"test_MSE": test_MSE,
"test_RMSE": test_rmse,
"test_NLL": test_nll,
"average_train_time_per_epoch": avg_train_time_per_epoch
})
elif cfg.task.task_type == 'classification':
test_accuracy, test_cross_entropy, test_entropy, test_nll, test_ece, test_brier, test_AUROC = classification_evaluate_modellist(modellist, dataloader=test_dataloader, device=device, config=cfg)
print(f"Test Acc: {test_accuracy:.4f}, Test CrossEntropy: {test_cross_entropy:.4f}, Test Entropy: {test_entropy:.4f}, Test NLL: {test_nll:.4f}, Test ECE: {test_ece:.4f}, Test Brier: {test_brier:.4f}, Test AUROC: {test_AUROC:.4f}, Avg Time / Epoch: {avg_train_time_per_epoch:.4f} ")
# Log classification test metrics
if cfg.experiment.wandb_logging:
wandb.run.summary.update({
"test_accuracy": test_accuracy,
"test_cross_entropy": test_cross_entropy,
"test_entropy": test_entropy,
"test_NLL": test_nll,
"test_ECE": test_ece,
"test_Brier": test_brier,
"test_AUROC": test_AUROC,
"average_train_time_per_epoch": avg_train_time_per_epoch
})
# Ensure avg_train_time_per_epoch is a tensor and move to CPU
if isinstance(avg_train_time_per_epoch, torch.Tensor):
avg_train_time_per_epoch = avg_train_time_per_epoch.cpu()
#metrics_array[fold] = [test_MSE, test_nll, avg_train_time_per_epoch]
if cfg.task.task_type == 'regression':
if isinstance(test_MSE, torch.Tensor):
test_MSE = test_MSE.cpu()
if isinstance(test_nll, torch.Tensor):
test_nll = test_nll.cpu()
metrics_array[fold] = [test_MSE, test_nll, avg_train_time_per_epoch]
elif cfg.task.task_type == 'classification':
if isinstance(test_accuracy, torch.Tensor):
test_accuracy = test_accuracy.cpu()
if isinstance(test_cross_entropy, torch.Tensor):
test_cross_entropy = test_cross_entropy.cpu()
if isinstance(test_nll, torch.Tensor):
test_nll = test_nll.cpu()
if isinstance(test_ece, torch.Tensor):
test_ece = test_ece.cpu()
if isinstance(test_brier, torch.Tensor):
test_brier = test_brier.cpu()
if isinstance(test_AUROC, torch.Tensor):
test_AUROC = test_AUROC.cpu()
metrics_array[fold] = [test_accuracy,test_cross_entropy, test_nll, test_ece, test_brier,test_AUROC, avg_train_time_per_epoch]
# Save model checkpoint if required
if cfg.experiment.save_model:
base_save_path = cfg.experiment.base_save_path
modellist_path = os.path.join(base_save_path, model_path)
# Ensure the directory exists
if not os.path.exists(modellist_path):
os.makedirs(modellist_path)
save_path = os.path.join(modellist_path, f"model_fold{fold+1}.pt")
combined_state_dict = {f'model_{i}': model.state_dict() for i, model in enumerate(modellist)}
torch.save(combined_state_dict, save_path)
print(f"Combined model state dictionary saved to {save_path}")
if fold== 0 and cfg.task.dataset in ["sine", "gap"]:
# Constructing the save path
save_path = "images/" + cfg.task.dataset
plot_name = cfg.experiment.method
if cfg.experiment.method == "SVN":
# Assuming you have a way to specify or retrieve the type of Hessian calculation
# For demonstration, let's say it's another configuration parameter under experiment
hessian_type = cfg.SVN.hessian_calc # This should be defined in your config
plot_name += f"_n_epchs{cfg.experiment.num_epochs}_n_samp{cfg.experiment.n_samples}_{hessian_type}_lr{cfg.experiment.lr}_parts{cfg.experiment.n_particles}.png"
else:
plot_name += f"_n_epchs{cfg.experiment.num_epochs}_n_samp{cfg.experiment.n_samples}_lr{cfg.experiment.lr}_parts{cfg.experiment.n_particles}.png"
full_save_path = f"{save_path}/{plot_name}"
print("Save path:", full_save_path)
plot_modellist(modellist, x_train_split, y_train_split, x_test, y_test, full_save_path)
if cfg.experiment.wandb_logging:
wandb.finish()
# After all folds are complete, aggregate your metrics across folds to evaluate overall performance
#aggregate_metrics(metrics_list)
# Convert metrics_list to a numpy array for easier calculation of mean and std
#metrics_array = np.array(metrics_list)
#metrics_array = np.array([metric.detach().cpu() for metric in metrics_list])
# Calculate mean and standard deviation for each metric across all folds
metrics_mean = metrics_array.mean(axis=0)
metrics_std = metrics_array.std(axis=0) / np.sqrt(cfg.experiment.n_splits)
if cfg.task.task_type == 'regression':
print(f"Average Test MSE: {metrics_mean[0]:.2f} ± {metrics_std[0]:.2f}, Average Test NLL: {metrics_mean[1]:.2f} ± {metrics_std[1]:.2f}, Avg Time / Epoch: {metrics_mean[2]:.2f} ± {metrics_std[2]:.2f}")
elif cfg.task.task_type == 'classification':
print(f"Avg Test Accuracy: {metrics_mean[0]:.2f} ± {metrics_std[0]:.2f}, Avg Test CrossEntr: {metrics_mean[1]:.2f} ± {metrics_std[1]:.2f}, Avg NLL: {metrics_mean[2]:.2f} ± {metrics_std[2]:.2f}, Avg Test ECE: {metrics_mean[3]:.2f} ± {metrics_std[3]:.2f}, Avg Test Brier: {metrics_mean[4]:.2f} ± {metrics_std[4]:.2f}, Avg AUROC: {metrics_mean[5]:.2f} ± {metrics_std[5]:.2f}, Avg Time / Epoch: {metrics_mean[6]:.2f} ± {metrics_std[6]:.2f}")
print('finish')