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training.py
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training.py
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import numpy as np
import gzip
import pickle
from pathlib import Path
import torch
import random
from gnn import VariableMappingGNN
import argparse
data_dir="data"
def preprocess_data_test_time(left_ast, right_ast):
left_edge_index_pairs = []
left_edge_types = []
for triple in left_ast['edges']:
left_edge_index_pairs.append([triple[0], triple[1]])
left_edge_types.append(triple[2])
left_node_types = [left_ast['nodes2types'][k] for k in left_ast['nodes2types']]
right_edge_index_pairs = []
right_edge_types = []
for triple in right_ast['edges']:
right_edge_index_pairs.append([triple[0], triple[1]])
right_edge_types.append(triple[2])
right_node_types = [right_ast['nodes2types'][k] for k in right_ast['nodes2types']]
# var_norm_index = {k: e for (e, k) in enumerate(left_ast['vars2id'])}
# var_norm_index2 = {k: e for (e, k) in enumerate(right_ast['vars2id'])}
left_node_types = torch.as_tensor(left_node_types)
right_node_types = torch.as_tensor(right_node_types)
left_edge_index_pairs = torch.as_tensor(left_edge_index_pairs)
right_edge_index_pairs = torch.as_tensor(right_edge_index_pairs)
left_edge_types = torch.as_tensor(left_edge_types)
right_edge_types = torch.as_tensor(right_edge_types)
return ((left_node_types, left_edge_index_pairs, left_edge_types, left_ast),
(right_node_types, right_edge_index_pairs, right_edge_types, right_ast))
def preprocess_data(left_ast, right_ast, varmap, sample_spec):
left_edge_index_pairs = []
left_edge_types = []
for triple in left_ast['edges']:
if triple[2] in ablation_edges:
left_edge_index_pairs.append([triple[0], triple[1]])
left_edge_types.append(triple[2])
max_node_described = -1
for e in left_edge_index_pairs:
if e[0] > max_node_described:
max_node_described = e[0]
if e[1] > max_node_described:
max_node_described = e[1]
left_node_types = [left_ast['nodes2types'][k] for k in left_ast['nodes2types']]
right_edge_index_pairs = []
right_edge_types = []
for triple in right_ast['edges']:
right_edge_index_pairs.append([triple[0], triple[1]])
right_edge_types.append(triple[2])
max_node_described = -1
for e in right_edge_index_pairs:
if e[0] > max_node_described:
max_node_described = e[0]
if e[1] > max_node_described:
max_node_described = e[1]
right_node_types = [right_ast['nodes2types'][k] for k in right_ast['nodes2types']]
var_norm_index = {k: e for (e, k) in enumerate(left_ast['vars2id'])}
var_norm_index2 = {k: e for (e, k) in enumerate(right_ast['vars2id'])}
labels = []
# TODO ATTENTION varmap needs to be reversed!
varmap = {varmap[key]:key for key in varmap} # reversal
for k in varmap:
labels.append(var_norm_index2[varmap[k]])
left_node_types = torch.as_tensor(left_node_types)
right_node_types = torch.as_tensor(right_node_types)
left_edge_index_pairs = torch.as_tensor(left_edge_index_pairs)
right_edge_index_pairs = torch.as_tensor(right_edge_index_pairs)
left_edge_types = torch.as_tensor(left_edge_types)
right_edge_types = torch.as_tensor(right_edge_types)
return ((left_node_types, left_edge_index_pairs, left_edge_types, left_ast), (right_node_types, right_edge_index_pairs, right_edge_types, right_ast), labels, sample_spec)
if __name__ == "__main__":
torch.set_num_threads(20)
parser = argparse.ArgumentParser(description='Train a model.')
parser.add_argument('--error', type=str,
help='What bug we want to train on.')
parser.add_argument('--gpu', type=str,
help='Which gpu to use')
parser.add_argument('--expname', type=str,
help='Name of the experiment; defines where trained model is stored.')
parser.add_argument('--samplecap', type=int,
help='How many samples to take from each folder.')
parser.add_argument('--edgetypes', type=str, help='comma-separated list of which edgetypes to used')
args = parser.parse_args()
student_sample_cap = 1
path = Path(f"{data_dir}/variable-alignment/mutilated_programs/year-1/lab02/")
device = torch.device(f"cuda:{args.gpu}" if torch.cuda.is_available() else "cpu")
# error = "wrong_comp_op"
# error = "variable_misuse"
# error = "expression_deletion"
# error = "all"
errorlist = ["wrong_comp_op", "variable_misuse", "expression_deletion", "all"]
ablation_edges = [int(k) for k in args.edgetypes.split(",")]
error = args.error
if error not in errorlist:
raise ValueError("This error is not supported, did you misspell? Use one of these: wrong_comp_op, variable_misuse, expression_deletion or all")
expname = f"{args.expname}_{error}_cap{args.samplecap}"
if error == "all":
use_all_errors = True
else:
use_all_errors = False
all_exercises = []
for p in path.glob("*"):
if p.is_dir():
all_exercises.append(p)
print(all_exercises)
print(len(all_exercises))
mut_paths = []
for exercise in all_exercises:
ex_path = Path(exercise)
for p in ex_path.glob("*"):
if p.is_dir():
mut_paths.append(p)
print(mut_paths)
print(len(mut_paths))
mutil_paths = []
for mut in mut_paths:
mut_path = Path(mut)
for p in mut_path.glob("*"):
if p.is_dir():
if p.name == error or use_all_errors:
mutil_paths.append(p)
print(mutil_paths)
print(len(mutil_paths))
student_paths = []
for mutil in mutil_paths:
mutil_path = Path(mutil)
for p in mutil_path.glob("*"):
if p.is_dir():
student_paths.append(p)
print(student_paths)
print(len(student_paths))
student_names = []
for stu in student_paths:
# print(stu.name)
if not stu.name in student_names:
student_names.append(stu.name)
# make deterministic
#student_names = sorted(student_names)
print(len(student_names))
# assert 2 > 3
print(len(student_paths))
student_paths = [k for k in student_paths if not "tmp_input_file" in str(k.absolute())]
print(len(student_paths))
# assert 2 > 3
random.seed(18)
student_names.remove("reference_implementation")
#random.shuffle(sorted(student_names)) # sort first to ensure determinism
random.shuffle(student_names)
no_stud = len(student_names)
train_end = int(np.floor(0.8*no_stud))
train_set = student_names[:train_end]
val_set = student_names[train_end:]
print(val_set)
#valset used last
#valset ['stu_002', 'stu_014', 'stu_009', 'stu_015', 'stu_008']
#assert 2 > 3
training_data = []
val_data = []
for p in student_paths:
print(p)
assert "year-2" not in str(p.absolute())
for p in student_paths:
print(p.name)
if p.name in train_set:
print(f"{p.name} in train set")
training_data.append(p)
elif p.name in val_set:
print(f"{p.name} in val set")
val_data.append(p)
else:
print(f"{p.name} not found")
# assert (len(val_data) + len(training_data)) == len(student_paths)
# training_data = random.sample(training_data, k = 400)
# val_data = random.sample(val_data, k=20)
print(len(training_data))
print(len(val_data))
print(len(val_data) + len(training_data))
#assert 2 > 3
error_files = []
train_samples = []
for path in training_data:
files_c = []
files_ast = []
files_varmap = []
for p in path.rglob("*"):
if p.name.startswith("var_map"):
files_varmap.append(p)
random.shuffle(files_varmap)
for sample_specification in files_varmap[:student_sample_cap]:
original_mutation_file = sample_specification.parents[0].name
prefix = "var_map-"
postfix = ".pkl.gz"
tmp_str = str(sample_specification.name)[len(prefix):]
tmp_str = tmp_str[:(len(tmp_str) - len(postfix))]
left, right = tmp_str.split("_")
if not all([k == '0' for k in right]):
# if not (int(left) == 0 and int(right) == 0):
left_ast_file = str(sample_specification.parents[0]) + "/" + "ast-" + right + postfix
right_ast_file = str(sample_specification.parents[0]) + "/" + "ast-" + left + postfix
student = str(right_ast_file).replace("mutilated_programs", "C-Pack-IPAs_blocks/correct_submissions")
student = Path(student)
student_stump = student.parents[0].parents[0].parents[0].parents[0].parents[0]
student = Path(right_ast_file).parents[0].parents[0].name
correct_file_path = str(student_stump.absolute()) + "/ast-" + student + ".pkl.gz"
right_ast_file = correct_file_path
# print(left_ast_file)
# print(right_ast_file)
# assert 2 > 3
with gzip.open(sample_specification, 'rb') as f:
varmap = pickle.load(f)
print(varmap)
with gzip.open(left_ast_file, 'rb') as f:
left_ast = pickle.load(f)
with gzip.open(right_ast_file, 'rb') as f:
right_ast = pickle.load(f)
# perhaps also add the right to left combo
train_samples.append(preprocess_data(left_ast, right_ast, varmap, sample_specification))
# assert 2 > 3
val_samples = []
for path in val_data:
files_c = []
files_ast = []
files_varmap = []
for p in path.rglob("*"):
if p.name.startswith("var_map"):
files_varmap.append(p)
for sample_specification in files_varmap[:student_sample_cap]:
print(sample_specification)
# try:
prefix = "var_map-"
postfix = ".pkl.gz"
tmp_str = str(sample_specification.name)[len(prefix):]
tmp_str = tmp_str[:(len(tmp_str) - len(postfix))]
left, right = tmp_str.split("_")
print("Program Names: ")
print(left, right) #
if not all([k == '0' for k in right]):
left_ast_file = str(sample_specification.parents[0]) + "/" + "ast-" + right + postfix
right_ast_file = str(sample_specification.parents[0]) + "/" + "ast-" + left + postfix
student = str(right_ast_file).replace("mutilated_programs", "C-Pack-IPAs_blocks/correct_submissions")
student = Path(student)
student_stump = student.parents[0].parents[0].parents[0].parents[0].parents[0]
student = Path(right_ast_file).parents[0].parents[0].name
correct_file_path = str(student_stump.absolute()) + "/ast-" + student + ".pkl.gz"
with gzip.open(sample_specification, 'rb') as f:
varmap = pickle.load(f)
with gzip.open(left_ast_file, 'rb') as f:
left_ast = pickle.load(f)
with gzip.open(right_ast_file, 'rb') as f:
right_ast = pickle.load(f)
# perhaps also add the right to left combo
val_samples.append(preprocess_data(left_ast, right_ast, varmap, sample_specification))
# Some global information about which types of nodes exist
with gzip.open("types2int.pkl.gz", 'rb') as f:
node_type_mapping = pickle.load(f)
print(node_type_mapping)
# find maximum index
num_types = node_type_mapping['diff_types']
print(num_types)
print(error_files)
print(len(error_files))
gnn = VariableMappingGNN(num_types, device).to(device)
loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(gnn.parameters())
print(len(training_data), len(train_samples))
print(len(val_data), len(val_samples))
assert 2>3
acc_list = []
fc_l = []
from tqdm import tqdm
for i in tqdm(range(20)):
global_representation_list = []
global_orig_sample_list = []
ep_corr = 0
ep_total = 0
random.shuffle(train_samples)
for j in tqdm(range(len(train_samples))):
gnn.train_step(train_samples[j], loss, optimizer)
random.shuffle(val_samples)
fully_correct_list = []
with torch.no_grad():
for j in tqdm(range(len(val_samples))):
corr, total, fully_correct = gnn.eval_step(val_samples[j], loss)
ep_corr += corr
ep_total += total
fully_correct_list.append(fully_correct)
print(f"Epoch Evaluation Accuracy: {ep_corr} / {ep_total}")
print(f"Epoch Evaluation Samples Fully Correct: {np.mean(fully_correct_list) * 100} %")
fc_l.append(np.mean(fully_correct_list) * 100)
acc_list.append(float(ep_corr) / float(ep_total))
torch.save(gnn.state_dict(), f"{data_dir}/{expname}.pt")
# Example command
# python -u training.py --error variable_misuse --gpu 0 --samplecap 1 --expname dev_cl