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""" Finetuning example.
"""
from __future__ import print_function
import sys
import numpy as np
from os.path import abspath, dirname
sys.path.insert(0, dirname(dirname(abspath(__file__))))
import json
import math
from torchmoji.model_def import torchmoji_transfer
from torchmoji.global_variables import PRETRAINED_PATH, VOCAB_PATH
from torchmoji.finetuning import (
load_benchmark,
finetune)
from torchmoji.class_avg_finetuning import class_avg_finetune
def roundup(x):
return int(math.ceil(x / 10.0)) * 10
# Format: (dataset_name,
# path_to_dataset,
# nb_classes,
# use_f1_score)
DATASETS = [
#('SE0714', '../data/SE0714/raw.pickle', 3, True),
#('Olympic', '../data/Olympic/raw.pickle', 4, True),
#('PsychExp', '../data/PsychExp/raw.pickle', 7, True),
#('SS-Twitter', '../data/SS-Twitter/raw.pickle', 2, False),
('SS-Youtube', '../data/SS-Youtube/raw.pickle', 2, False),
#('SE1604', '../data/SE1604/raw.pickle', 3, False), # Excluded due to Twitter's ToS
#('SCv1', '../data/SCv1/raw.pickle', 2, True),
#('SCv2-GEN', '../data/SCv2-GEN/raw.pickle', 2, True)
]
RESULTS_DIR = 'results'
# 'new' | 'last' | 'full' | 'chain-thaw'
FINETUNE_METHOD = 'last'
VERBOSE = 1
nb_tokens = 50000
nb_epochs = 1000
epoch_size = 1000
with open(VOCAB_PATH, 'r') as f:
vocab = json.load(f)
for rerun_iter in range(5):
for p in DATASETS:
# debugging
assert len(vocab) == nb_tokens
dset = p[0]
path = p[1]
nb_classes = p[2]
use_f1_score = p[3]
if FINETUNE_METHOD == 'last':
extend_with = 0
elif FINETUNE_METHOD in ['new', 'full', 'chain-thaw']:
extend_with = 10000
else:
raise ValueError('Finetuning method not recognised!')
# Load dataset.
data = load_benchmark(path, vocab, extend_with=extend_with)
(X_train, y_train) = (data['texts'][0], data['labels'][0])
(X_val, y_val) = (data['texts'][1], data['labels'][1])
(X_test, y_test) = (data['texts'][2], data['labels'][2])
weight_path = PRETRAINED_PATH if FINETUNE_METHOD != 'new' else None
nb_model_classes = 2 if use_f1_score else nb_classes
model = torchmoji_transfer(
nb_model_classes,
weight_path,
extend_embedding=data['added'])
print(model)
# Training
print('Training: {}'.format(path))
if use_f1_score:
model, result = class_avg_finetune(model, data['texts'],
data['labels'],
nb_classes, data['batch_size'],
FINETUNE_METHOD,
verbose=VERBOSE)
else:
model, result = finetune(model, data['texts'], data['labels'],
nb_classes, data['batch_size'],
FINETUNE_METHOD, metric='acc',
verbose=VERBOSE)
# Write results
if use_f1_score:
print('Overall F1 score (dset = {}): {}'.format(dset, result))
with open('{}/{}_{}_{}_results.txt'.
format(RESULTS_DIR, dset, FINETUNE_METHOD, rerun_iter),
"w") as f:
f.write("F1: {}\n".format(result))
else:
print('Test accuracy (dset = {}): {}'.format(dset, result))
with open('{}/{}_{}_{}_results.txt'.
format(RESULTS_DIR, dset, FINETUNE_METHOD, rerun_iter),
"w") as f:
f.write("Acc: {}\n".format(result))
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