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main.py
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from core.no_cheb.helper import *
from core.no_cheb.training import *
from core.no_cheb.models import *
from datetime import datetime
from prettytable import PrettyTable
import math
def print_results(maxpool_acc, maxpool_loss, maxpool_time, meanpool_acc, meanpool_loss, meanpool_time, diffpool_acc,
diffpool_loss, diffpool_time):
t = PrettyTable()
t.title = "Results"
t.field_names = ["Pooling", "Accuracy (%)", "Loss", "Time (s)"]
t.add_row( ["MaxPool", round( maxpool_acc * 100, 2 ), round( maxpool_loss, 2 ), round( maxpool_time, 2 )] )
t.add_row( ["MeanPool", round( meanpool_acc * 100, 2 ), round( meanpool_loss, 2 ), round( meanpool_time, 2 )] )
t.add_row( ["DiffPool", round( diffpool_acc * 100, 2 ), round( diffpool_loss, 2 ), round( diffpool_time, 2 )] )
print( t )
def main():
batch_size = 32
epochs = 100
dataset_name = "PROTEINS" # PROTEINS, ENZYMES
k_validation = 10
start = datetime.now()
dataset = read_graphfile( "datasets", dataset_name, max_nodes=None )
avg_num_nodes = calculate_avg_nodes( dataset )
train, val, test, num_classes = make_train_test_val( dataset )
# print_dataset_stats(dataset, train, val, test, dataset_name, avg_num_nodes, num_classes)
t = PrettyTable( header=False )
t.title = dataset_name
# t.field_names = ["Pooling"]
t.add_row( ["Number of graphs", len( dataset )] )
t.add_row( ["Number of classes", num_classes] )
t.add_row( ["Average number of nodes", math.ceil( avg_num_nodes )] )
t.add_row( ["Training samples (60%)", len( train[0] )] )
t.add_row( ["Validation samples (20%)", len( val[0] )] )
t.add_row( ["Testing samples (20%)", len( test[0] )] )
print( t )
train, val, test = preprocess_dataset( train, val, test )
end = datetime.now()
print( f"\nPreprocessing time: {(end - start).total_seconds()} seconds" )
print( "GCN32Max" )
maxpool_acc, maxpool_loss, maxpool_time = k_fold_validation( lambda:
train_model( GCN32Max( num_classes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print( "GCN32Mean" )
meanpool_acc, meanpool_loss, meanpool_time = k_fold_validation( lambda:
train_model( GCN32Mean( num_classes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print( "GCN32Diff" )
diffpool_acc, diffpool_loss, diffpool_time = k_fold_validation( lambda:
train_model(
GCN32Diff( num_classes, avg_num_nodes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print_results( maxpool_acc, maxpool_loss, maxpool_time, meanpool_acc, meanpool_loss, meanpool_time, diffpool_acc,
diffpool_loss, diffpool_time )
print( "GCN3232Max" )
maxpool_acc, maxpool_loss, maxpool_time = k_fold_validation( lambda:
train_model( GCN3232Max( num_classes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print( "GCN3232Mean" )
meanpool_acc, meanpool_loss, meanpool_time = k_fold_validation( lambda:
train_model( GCN3232Mean( num_classes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print( "GCN3232Diff" )
diffpool_acc, diffpool_loss, diffpool_time = k_fold_validation( lambda:
train_model(
GCN3232Diff( num_classes, avg_num_nodes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print_results( maxpool_acc, maxpool_loss, maxpool_time, meanpool_acc, meanpool_loss, meanpool_time, diffpool_acc,
diffpool_loss, diffpool_time )
print( "GCN3264Max" )
maxpool_acc, maxpool_loss, maxpool_time = k_fold_validation( lambda:
train_model( GCN3264Max( num_classes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print( "GCN3264Mean" )
meanpool_acc, meanpool_loss, meanpool_time = k_fold_validation( lambda:
train_model( GCN3264Mean( num_classes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print( "GCN3264Diff" )
diffpool_acc, diffpool_loss, diffpool_time = k_fold_validation( lambda:
train_model(
GCN3264Diff( num_classes, avg_num_nodes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print_results( maxpool_acc, maxpool_loss, maxpool_time, meanpool_acc, meanpool_loss, meanpool_time, diffpool_acc,
diffpool_loss, diffpool_time )
print( "GCN3232Reshape" )
reshape_acc, reshape_loss, reshape_time = k_fold_validation( lambda:
train_model(
GCN3232Reshape( num_classes, avg_num_nodes ),
train,
val,
test,
epochs,
batch_size ),
k=k_validation )
print_results( maxpool_acc, maxpool_loss, maxpool_time, meanpool_acc, meanpool_loss, meanpool_time, reshape_acc,
reshape_loss, reshape_time )
return
main()