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cross_val.py
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cross_val.py
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# -*- coding: utf-8 -*-
import math
import numpy as np
import torch
from graph_sampler import GraphSampler
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Returns train, validation and test sets.
def datasets_splits(folds, args, val_idx):
train = []
validation = []
test = []
train_folds = []
for i in range(len(folds)):
if i==val_idx:
test.extend(folds[i])
else:
train_folds.append(folds[i])
validation.extend(train_folds[0])
for i in range(1, len(train_folds)):
train.extend(train_folds[i])
return train, validation, test
def model_selection_split(train, validation, args):
print('Num training graphs: ', len(train),
'; Num test graphs: ', len(validation))
# minibatch
dataset_sampler = GraphSampler(train)
train_dataset_loader = torch.utils.data.DataLoader(
dataset_sampler,
batch_size = 1,
shuffle = False)
dataset_sampler = GraphSampler(validation)
val_dataset_loader = torch.utils.data.DataLoader(
dataset_sampler,
batch_size = 1,
shuffle = False)
train_mean, train_median = get_stats(train)
if(args.threshold == 'median'):
threshold_value = train_median
elif(args.threshold == 'mean'):
threshold_value = train_mean
else:
threshold_value = 0.0
return train_dataset_loader, val_dataset_loader, threshold_value
def model_assessment_split(train, validation, test, args):
train.extend(validation)
print('Num training graphs: ', len(train),
'; Num test graphs: ', len(test))
# minibatch
dataset_sampler = GraphSampler(train)
train_dataset_loader = torch.utils.data.DataLoader(
dataset_sampler,
batch_size = 1,
shuffle = False)
dataset_sampler = GraphSampler(test)
test_dataset_loader = torch.utils.data.DataLoader(
dataset_sampler,
batch_size = 1,
shuffle = False)
train_mean, train_median = get_stats(train)
if(args.threshold == 'median'):
threshold_value = train_median
if(args.threshold == 'mean'):
threshold_value = train_mean
return train_dataset_loader, test_dataset_loader, threshold_value
def two_shot_loader(train, test, args):
print('Num training graphs: ', len(train),
'; Num test graphs: ', len(test))
# minibatch
dataset_sampler = GraphSampler(train)
train_dataset_loader = torch.utils.data.DataLoader(
dataset_sampler,
batch_size = 1,
shuffle = False)
dataset_sampler = GraphSampler(test)
val_dataset_loader = torch.utils.data.DataLoader(
dataset_sampler,
batch_size = 1,
shuffle = False)
train_mean, train_median = get_stats(train)
if(args.threshold == 'median'):
threshold_value = train_median
elif(args.threshold == 'mean'):
threshold_value = train_mean
else:
threshold_value = 0.0
return train_dataset_loader, val_dataset_loader, threshold_value
# Splits the dataset into k-folds
def stratify_splits(graphs, args):
graphs_0 = []
graphs_1 = []
for i in range(len(graphs)):
if graphs[i]['label'] == 0:
graphs_0.append(graphs[i])
if graphs[i]['label'] == 1:
graphs_1.append(graphs[i])
graphs_0_folds = []
graphs_1_folds = []
pop_0_fold_size = math.floor(len(graphs_0) / args.cv_number)
pop_1_fold_size = math.floor(len(graphs_1) / args.cv_number)
graphs_0_folds = [graphs_0[i:i + pop_0_fold_size] for i in range(0, len(graphs_0), pop_0_fold_size)]
graphs_1_folds = [graphs_1[i:i + pop_1_fold_size] for i in range(0, len(graphs_1), pop_1_fold_size)]
folds = []
for i in range(args.cv_number):
fold = []
fold.extend(graphs_0_folds[i])
fold.extend(graphs_1_folds[i])
folds.append(fold)
if len(graphs_0_folds) > args.cv_number:
folds[args.cv_number-1].extend(graphs_0_folds[args.cv_number])
if len(graphs_1_folds) > args.cv_number:
folds[args.cv_number-1].extend(graphs_1_folds[args.cv_number])
return folds
def get_stats(list_train):
train_features = []
for i in range(len(list_train)):
ut_x_indexes = np.triu_indices(list_train[i]['adj'].shape[0], k=1)
ut_x = list_train[i]['adj'][ut_x_indexes]
for i in range(len(list_train)):
ut_x_indexes = np.triu_indices(list_train[i]['adj'].shape[0], k=1)
ut_x = list_train[i]['adj'][ut_x_indexes]
train_features.extend(list(ut_x))
train_features = np.array(train_features)
train_mean = np.mean(train_features)
train_median = np.median(train_features)
return train_mean, train_median