def predict(self, fn):
'''样本预测;'''
pt = Preprocessor()
tmp = pt.img2vec(fn)
X_test = tmp.reshape(1, -1)
ans = self.clf.predict(X_test)
return ans
def main(args):
if args.dataset == 'Dolphin18k':
from utils import Preprocessor as Preprocessor
elif args.dataset == 'Math23k':
from utils import Math23kPreprocessor as Preprocessor
else:
logging.error('Not compitable dataset!')
return
if args.index is not None:
with open(args.index) as f:
shuffled_index = json.load(f)
else:
shuffled_index = None
preprocessor = Preprocessor(args.embedding_path)
train, valid = preprocessor.get_train_valid_dataset(args.data_path,
args.valid_ratio,
index=shuffled_index,
char_based=args.char_based)
with open(args.output, 'wb') as f:
pickle.dump({'train': train,
'valid': valid,
'preprocessor': preprocessor}, f)
def eval_db_agent(env, params):
if params['use_preproc']:
preprocessor = Preprocessor(params['state_dim'], params['history'], params['use_luminance'],
params['resize_shape'])
params['state_dim'] = preprocessor.state_shape
else:
preprocessor = None
agent = VAE(params['state_dim'], params['action_dim'])
if params['use_cuda']:
agent = agent.cuda()
agent.load_state_dict(torch.load('./agents/{0}_{1}'.format(params['arch'], params['env_name'])))
else:
agent.load_state_dict(
torch.load('./agents/{0}_{1}'.format(params['arch'], params['env_name']), map_location='cpu'))
agent.eval()
agent_steps = 0
episode_rewards = []
start = time.time()
for episode in xrange(1, params['num_episodes'] + 1):
env_state = env.reset()
episode_reward = 0.0
for t in xrange(1, params['max_steps'] + 1):
if params['env_render']:
env.render()
if preprocessor:
state = preprocessor.process_state(env_state)
else:
state = env_state
var_state = createVariable(state, use_cuda=params['use_cuda'])
action, state_val = agent.sample_action_eval(var_state)
reward = 0.0
for _ in range(1):
env_state, r, terminal, _ = env.step(action)
reward += r
if terminal:
break
episode_reward += reward
if terminal:
break
episode_rewards.append(episode_reward)
agent_steps += t
if preprocessor:
preprocessor.reset()
print 'Episode {0} | Total Steps {1} | Total Reward {2} | Mean Reward {3} | Total Time {4}' \
.format(episode, agent_steps, episode_reward, sum(episode_rewards[-100:]) / 100,
timeSince(start, episode / params['num_episodes']))
class TestFeatureAdder(unittest2.TestCase):
def setUp(self):
self.stats_calc = StatsCalculator()
self.preprocessor = Preprocessor()
self.feature_adder = FeatureAdder()
self.col_names = [f'feature_{i}' for i in range(FEATURES)]
def tearDown(self):
self.stats_calc = None
self.preprocessor = None
self.feature_adder = None
def _get_df(self):
df = pd.read_csv('data/train.tsv', sep='\t')
df = self.preprocessor.split_features(df)
df = self.preprocessor.f_to_int(df)
return df
def test_max_index_feature(self):
"""
Test that new feature 'max_feature_2_index' lies in proper range and has dtype 'int64'
"""
df = self._get_df()
new_feature = 'max_feature_2_index'
df = self.feature_adder.max_index_feature(df)
valid_range, valid_dtype = (0, 255), 'int64'
assert df[new_feature].between(*valid_range).all() and df[new_feature].dtype == valid_dtype, \
"max_feature_2_index feature not in range OR has wrong dtype"
def test_abs_mean_diff_feature(self):
"""
Test that new feature 'max_feature_2_abs_mean_diff' is valid
"""
df = self._get_df()
df = self.feature_adder.max_index_feature(df)
new_feature = 'max_feature_2_abs_mean_diff'
cols = np.array(self.col_names)[df['max_feature_2_index'].values]
train_stats = find_train_stats('data/train.tsv', chunksize=10000)
df = self.feature_adder.abs_mean_diff_feature(
df.loc[:, df.columns != 'id_job'], train_stats)
results = []
for i, col in enumerate(cols):
# keep in mind outliers in test data
lower_bound, upper_bound = 0, train_stats[col]['std']
results.append(lower_bound <= df[new_feature][i] <= upper_bound)
self.assertTrue(
np.all(results),
"max_feature_2_index feature not in expected range OR has wrong dtype"
)
def main(test_csv, test_target_csv, prediction_csv, model_dir):
start_time = time.time()
# load model
model_config_filename = os.path.join(model_dir, 'model_config.pkl')
metric_file = os.path.join(model_dir, 'rating.txt')
with open(model_config_filename, 'rb') as fin:
model_config = pickle.load(fin)
# read dataset
df = pd.read_csv(test_csv)
print('Dataset read, shape {}'.format(df.shape))
line_id = df['line_id']
preprocessor = Preprocessor(model_config['features'])
df_X = preprocessor.transform(df)
model = model_config['model']
if model_config['mode'] == 'regression':
df['prediction'] = model.predict(df_X.values)
elif model_config['mode'] == 'classification':
df['prediction'] = model.predict_proba(df_X.values)[:, 1]
df['line_id'] = line_id
df[['line_id', 'prediction']].to_csv(prediction_csv, index=False)
print('Prediction time: {}'.format(time.time() - start_time))
if test_target_csv:
def save_metric(metric):
with open(metric_file, 'a') as f:
f.write('{}\n'.format(metric))
# read targets
test = pd.read_csv(test_target_csv)
print('Read targets, shape {}'.format(test.shape))
if model_config['mode'] == 'regression':
pred = preprocessor.target_inverse_transform(df['prediction'])
mse = np.mean((test.target - pred)**2)
r_2 = 1 - mse/np.std(test.target)**2
print('MSE: {}'.format(mse))
print('R^2: {}'.format(r_2))
save_metric(r_2)
elif model_config['mode'] == 'classification':
auc = roc_auc_score(test.target, df['prediction'])
print('AUC: {}'.format(auc))
save_metric(auc)
def run():
pt = Preprocessor()
tr = Trainer()
X_train, y_train = pt.load_data()
X_test, y_test = pt.load_data("mnist_test_data.npz")
x1 = X_train.reshape((-1, 28, 28, 1))
x2 = X_test.reshape((-1, 28, 28, 1))
y1 = keras.utils.to_categorical(y_train, len(np.unique(y_train)))
y2 = keras.utils.to_categorical(y_test, len(np.unique(y_test)))
clf = tr.cnn(x1, y1, x2, y2)
tr.save(clf, "cnn_mnist_keras.h5")
return clf
def train_agent(cmdl):
step_cnt = 0
ep_cnt = 0
preprocess = Preprocessor(cmdl.env_class).transform
env = utils.get_new_env(cmdl.env_name)
agent = get_agent(cmdl.agent.name)(env.action_space, cmdl.agent)
display_setup(env, cmdl)
start_time = time.time()
while step_cnt < cmdl.training.step_no:
ep_cnt += 1
o, r, done = env.reset(), 0, False
s = preprocess(o)
while not done:
a = agent.evaluate_policy(s)
o, r, done, _ = env.step(a)
_s, _a = s, a
s = preprocess(o)
agent.improve_policy(_s, _a, r, s, done)
step_cnt += 1
agent.gather_stats(r, done)
if ep_cnt % cmdl.report_freq == 0:
agent.display_stats(start_time)
agent.display_model_stats()
end_time = time.time()
display_stats(ep_cnt, step_cnt, end_time - start_time)
"""
def main():
# create prediction dataframe and score dataframe list
prediction_df = pd.DataFrame()
score_df_list = []
# transform, predict, score for each combination
for scaler, (model_class, params) in product(config.scalers, config.models):
# assign preprocessor and model
preprocessor = Preprocessor(scaler)
model = model_class(**params)
# process train, validation, test data
train_score, validation_score = process_train_data(preprocessor, model)
test_prediction, test_score = process_test_data(preprocessor, model)
# add predicted data to prediction dataframe
prediction_df[
f"{scaler.__name__}-{model}"
] = test_prediction
# add score dataframes to the list
score_df_list.extend([train_score, validation_score, test_score])
# concatenate score dataframes
report_df = pd.concat(score_df_list)
# save prediction and report to csv files
prediction_df.to_csv(config.prediction_file_path)
report_df.to_csv(config.report_file_path)
def __init__(self, config_path):
self.stream_api = StreamAPI.StreamAPI(config_path)
self.sorter = TweetSorter.TweetSorter()
self.preprocessor = Preprocessor.Preprocessor()
self.selector = AttributeSelector.AttributeSelector()
self.saver = ImageSaver.ImageSaver()
self.vader = SentimentAnalyzer.SentimentAnalyzer()
self.db = []
self.tweets_num = 0
def run_train():
# t0 = time.time()
pt = Preprocessor()
tr = Trainer_nn()
X_train, y_train = pt.get_data_labels()
X_test, y_test = pt.get_data_labels("test")
# X_train, y_train = pt.load_data()
# X_test, y_test = pt.load_data("mnist_test_data.npz")
clf = tr.mlp(X_train, y_train)
tr.save_model(clf, "mlp_mnist_Hu300x300ReluSgdIter100Acc96Sample60000.m")
tester = Tester("mlp_mnist_Hu300x300ReluSgdIter100Acc96Sample60000.m")
mt, score, repo = tester.clf_quality(X_test, y_test)
print(mt, score, repo)
return clf
def run_train():
t0 = time.time()
pt = Preprocessor()
tr = Trainer()
X_train, y_train = pt.get_data_labels()
X_test, y_test = pt.get_data_labels("test")
t1 = time.time()
print(t1 - t0)
clf = tr.svc(X_train, y_train)
print(time.time() - t1)
tr.save_model(clf, "mnist_svm.m")
tester = Tester("mnist_svm.m")
mt, score, repo = tester.clf_quality(X_test, y_test)
print(mt, score, repo)
return clf
def run():
pt = Preprocessor()
tr = Trainer()
ts = Tester()
t0 = time.time()
X_train, y_train = pt.load_data()
X_test, y_test = pt.load_data("mnist_test_data.npz")
X_train, y_train = make_shuffle(X_train, y_train)
X_test, y_test = make_shuffle(X_test, y_test)
X_train = X_train.reshape((-1, 1, 28, 28))
X_test = X_test.reshape((-1, 1, 28, 28))
print(time.time() - t0)
t1 = time.time()
clf = tr.net(X_train, y_train)
print(time.time() - t1)
acc = ts.get_acc(clf, X_test, y_test) #acc=97.8%
return clf, acc
def main():
emotionals, rationals = emotional_rational()
preprocessor = Preprocessor()
emotionals = preprocessor.parse_sentences(emotionals)
rationals = preprocessor.parse_sentences(rationals)
train_pos = emotionals[:len(emotionals) // 2]
train_neg = rationals[:len(rationals) // 2]
test_pos = emotionals[len(emotionals) // 2:]
test_neg = rationals[len(rationals) // 2:]
vectorizer = CountVectorizer()
X_train = vectorizer.fit_transform(train_pos + train_neg)
y_train = np.array([1] * len(train_pos) + [0] * len(train_neg))
X_test = vectorizer.transform(test_pos + test_neg)
y_test = np.array([1] * len(test_pos) + [0] * len(test_neg))
print('Vocabulary size : {}'.format(len(vectorizer.vocabulary_)))
nbsvm = NBSVM()
nbsvm.fit(X_train, y_train)
print('Test accuracy : {}'.format(nbsvm.score(X_test, y_test)))
y_pred = nbsvm.predict(X_test)
print('F1 score : {}'.format(f1_score(y_test, y_pred, average='macro')))
fpr, tpr, thresholds = roc_curve(y_test, y_pred, pos_label=1)
roc_auc = auc(fpr, tpr)
print('AUC of emotionals : {}'.format(roc_auc))
plot_roc_curve(fpr, tpr, roc_auc, 'nbsvm_emotional_roc.png')
fpr, tpr, thresholds = roc_curve(y_test, y_pred, pos_label=0)
roc_auc = auc(fpr, tpr)
print('AUC of rationals : {}'.format(roc_auc))
plot_roc_curve(fpr, tpr, roc_auc, 'nbsvm_rational_roc.png')
def evaluate_agent(crt_training_step, eval_env, eval_agent, policy, cmdl):
print("[Evaluator] Initializing at %d training steps:" %
crt_training_step)
agent = eval_agent
eval_env.get_crt_step(crt_training_step)
agent.policy_evaluation.policy.load_state_dict(policy.state_dict())
preprocess = Preprocessor(cmdl.env_class).transform
step_cnt = 0
o, r, done = eval_env.reset(), 0, False
while step_cnt < cmdl.evaluator.eval_steps:
s = preprocess(o)
a = agent.evaluate_policy(s)
o, r, done, _ = eval_env.step(a)
step_cnt += 1
if done:
o, r, done = eval_env.reset(), 0, False
def train_agent(cmdl):
step_cnt = 0
ep_cnt = 0
start_time = time.time()
env = utils.get_new_env(cmdl.env_name, cmdl)
eval_env = EvaluationMonitor(gym.make(cmdl.env_name), cmdl)
name = cmdl.agent.name
agent = get_agent(name)(env.action_space, cmdl.agent)
eval_agent = get_agent(name)(eval_env.action_space, cmdl.agent, False)
preprocess = Preprocessor(cmdl.env_class).transform
agent.display_setup(env, cmdl)
while step_cnt < cmdl.training.step_no:
ep_cnt += 1
o, r, done = env.reset(), 0, False
s = preprocess(o)
while not done:
a = agent.evaluate_policy(s)
o, r, done, _ = env.step(a)
_s, _a = s, a
s = preprocess(o)
agent.improve_policy(_s, _a, r, s, done)
step_cnt += 1
agent.gather_stats(r, done)
if step_cnt % cmdl.report_freq == 0:
agent.display_stats(start_time)
agent.display_model_stats()
gc.collect()
if step_cnt % cmdl.eval_freq == 0:
evaluate_agent(step_cnt, eval_env, eval_agent, agent.policy, cmdl)
end_time = time.time()
agent.display_final_report(ep_cnt, step_cnt, end_time - start_time)
def main():
emotionals, rationals = emotional_rational()
preprocessor = Preprocessor()
emotionals = preprocessor.parse_sentences(emotionals)
rationals = preprocessor.parse_sentences(rationals)
emotionals = emotionals[:len(emotionals)]
rationals = rationals[:len(emotionals)]
sentences = emotionals + rationals
Y = np.array([[0, 1]] * len(emotionals) + [[1, 0]] * len(rationals))
max_features = 200
tokenizer = Tokenizer(num_words=max_features, split=' ')
tokenizer.fit_on_texts(sentences)
X = tokenizer.texts_to_sequences(sentences)
X = pad_sequences(X, maxlen=MAX_LEN)
epochs = 15
# --- Add Features ---
dict_loader = EmotionalDict('dataset/nouns', 'dataset/verbs')
emotional_dict = dict_loader.load()
features_loader = AdditionalFeatures(emotionals+rationals, emotional_dict)
add_features = features_loader.emotional_features()
######################
x_aux_train = add_features[:848]
x_aux_test = add_features[848:]
model = build_model(x_aux_train.shape)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state=42)
print(X_train.shape, Y_train.shape)
print(X_test.shape, Y_test.shape)
batch_size = 32
model.fit({'main_input': X_train, 'add_input': x_aux_train}, Y_train, epochs=epochs, batch_size=batch_size, verbose=2)
score, acc = model.evaluate({'main_input': X_test, 'add_input': x_aux_test}, Y_test, verbose=2, batch_size=batch_size)
print('score: {}'.format(score))
print('acc: {}'.format(acc))
Y_pred = model.predict({'main_input': X_test, 'add_input': x_aux_test}, batch_size=1, verbose=2)
print(classification_report(Y_test[:, 1], np.round(Y_pred[:, 1]), target_names=['rationals', 'emotionals']))
fpr, tpr, _ = roc_curve(Y_test[:, 1], Y_pred[:, 1])
roc_auc = auc(fpr, tpr)
plot_roc_curve(fpr, tpr, roc_auc, 'roc.png')
cnf_matrix = confusion_matrix(Y_test[:, 1], np.round(Y_pred[:, 1]))
plot_confusion_matrix(cnf_matrix, ['rationals', 'emotionals'], 'cnf.png')
attention_vector = np.mean(get_activations(model, X_test, True, 'attention_vec')[0], axis=2).squeeze()
attention_vector = np.mean(attention_vector, axis=0)
import matplotlib.pyplot as plt
import pandas as pd
pd.DataFrame(attention_vector, columns=['attention (%)']).plot(kind='bar', title='Attention')
plt.savefig('attention_vec.png')
attention_vector_indices = np.argsort(attention_vector)[::-1]
word_index = tokenizer.word_index
word_index_inv = {v: k for k, v in word_index.items()}
with open('attention_word.txt', 'w') as f:
for i, attention_index in enumerate(attention_vector_indices, start=1):
try:
print('No.{} : {}'.format(i, word_index_inv[attention_index]), file=f)
except KeyError:
continue
from utils import Preprocessor
if __name__ == '__main__':
print('Initializing preprocessor')
preprocessor = Preprocessor()
print('Running preprocessor')
preprocessor.run()
print('Saving trie and inverted index')
preprocessor.save()
print('Preprocessor stats')
max_key_length = max(map(len, preprocessor.stats.keys()))
for k, v in preprocessor.stats.items():
print(f"{k.ljust(max_key_length)}: {v}")
print('Most common tokens')
max_key_length = max(map(len, preprocessor.most_common.keys()))
for token, token_count in preprocessor.most_common.items():
print(f"{repr(token).ljust(max_key_length+2)} appeared at least ONCE in {str(token_count).ljust(5)} documents")
print('Done')
def train(args):
vocab = Vocab.load(args.vocab, max_size=args.vocab_size)
data_reader = DataReader(data_dir=args.data_dir, shuffle=True)
preprocessor = Preprocessor(
predict_prev=args.predict_prev,
predict_cur=args.predict_cur,
predict_next=args.predict_next,
vocab=vocab, max_length=args.max_length, gpu=args.gpu)
model = SkipThought(
rnn_type=args.rnn_type, num_words=len(vocab),
word_dim=args.word_dim, hidden_dim=args.hidden_dim,
bidirectional=args.bidirectional,
predict_prev=args.predict_prev,
predict_cur=args.predict_cur,
predict_next=args.predict_next)
print(model)
if args.pretrained is not None:
print(f'Loading pretrained model from {args.pretrained}')
model.load_state_dict(
torch.load(args.pretrained,
map_location=lambda storage, loc: storage))
if args.gpu > -1:
model.cuda(args.gpu)
optimizer = optim.Adam(model.parameters())
summary_writer = SummaryWriter(os.path.join(args.save_dir, 'log'))
def add_scalar_summary(name, value, step):
summary_writer.add_scalar(tag=name, scalar_value=value,
global_step=step)
def add_text_summary(name, value, step):
summary_writer.add_text(tag=name, text_string=value,
global_step=step)
def variable(tensor, volatile=False):
return Variable(tensor, volatile=volatile)
def run_train_iter(batch):
if not model.training:
model.train()
src, tgt = preprocessor(batch)
src = (variable(src[0]), src[1])
for k in tgt:
tgt[k] = (variable(tgt[k][0]), tgt[k][1])
logits = model.forward(src=src, tgt=tgt)
loss = 0
for k in tgt:
logits_k = logits[k]
tgt_k = tgt[k]
loss = loss + basic.sequence_cross_entropy(
logits=logits_k[:-1], targets=tgt_k[0][1:],
length=tgt_k[1] - 1)
optimizer.zero_grad()
loss.backward()
clip_grad_norm(model.parameters(), max_norm=10)
optimizer.step()
return loss.data[0]
def ids_to_words(ids):
words = []
eos_id = vocab.stoi(vocab.eos)
for id_ in ids:
words.append(vocab.itos(id_))
if id_ == eos_id:
break
return words
def generate_using_decoder(name, src, max_length):
_, encoder_state = model.encoder(words=src[0], length=src[1])
if isinstance(encoder_state, tuple): # LSTM
encoder_state = encoder_state[0]
context = (encoder_state.transpose(0, 1).contiguous()
.view(-1, args.hidden_dim))
batch_size = src[1].size(0)
bos_id = vocab.stoi(vocab.bos)
bos = Variable(src[1].new(1, batch_size).fill_(bos_id))
decoder = model.get_decoder(name)
prev_pred = bos
done = torch.zeros(batch_size).byte()
hyps = []
prev_state = context.unsqueeze(0)
for t in range(max_length):
if done.all():
break
decoder_input = prev_pred
logit, prev_state = decoder(words=decoder_input,
prev_state=prev_state)
pred = logit.max(2)[1]
prev_pred = pred
hyps.append(pred.data)
hyps = torch.cat(hyps, dim=0).transpose(0, 1).tolist()
return hyps
def generate(batch):
# Greedy search
src, tgt = preprocessor(batch)
src = (variable(src[0]), src[1])
for k in tgt:
tgt[k] = (variable(tgt[k][0], volatile=True), tgt[k][1])
batch_size = src[0].size(1)
max_length = src[0].size(0) * 2
generated = {}
for k in tgt:
generated[k] = generate_using_decoder(
name=k, src=src, max_length=max_length)
results = []
for i in range(batch_size):
res = {'src': ' '.join(ids_to_words(src[0][:src[1][i], i].data)),
'tgt': {},
'out': {}}
for k in tgt:
res['tgt'][k] = ' '.join(ids_to_words(tgt[k][0][1:, i].data))
res['out'][k] = ' '.join(ids_to_words(generated[k][i]))
results.append(res)
return results
def generate_synthetic_batch(real_batch):
def sort_by_length(tgt_of_key):
sorted_length, sort_inds = tgt_of_key[1].sort(
dim=0, descending=True)
return tgt_of_key[0][:, sort_inds], sorted_length
# Forward: given prev, generate cur'
_, tgt = preprocessor(real_batch)
tgt_prev, tgt_prev_length = sort_by_length(tgt['prev'])
syn_src_fw = generate_using_decoder(
name='next',
src=(variable(tgt_prev[1:], volatile=True),
tgt_prev_length - 1),
max_length=args.max_length)
# Backward: given next, generate cur''
tgt_next, tgt_next_length = sort_by_length(tgt['next'])
syn_src_bw = generate_using_decoder(
name='prev',
src=(variable(tgt_next[1:], volatile=True),
tgt_next_length - 1),
max_length=args.max_length)
syn_batch_fw = []
syn_batch_bw = []
for i in range(len(real_batch)):
syn_src_fw_str = ' '.join(ids_to_words(syn_src_fw[i]))
syn_src_bw_str = ' '.join(ids_to_words(syn_src_bw[i]))
syn_batch_fw.append(
(real_batch[i][0], syn_src_fw_str, real_batch[i][2]))
syn_batch_bw.append(
(real_batch[i][0], syn_src_bw_str, real_batch[i][2]))
return syn_batch_fw, syn_batch_bw
global_step = 0
def print_samples():
model.eval()
num_samples = 2
samples = data_reader.next_batch(size=num_samples, peek=True)
syn_samples_fw, syn_samples_bw = generate_synthetic_batch(samples)
gen_results = generate(samples)
syn_gen_results_fw = generate(syn_samples_fw)
syn_gen_results_bw = generate(syn_samples_bw)
text_val = ''
for i, res in enumerate(gen_results):
text_val += f'* sample (real) #{i}\n'
text_val += f'\t* src: {res["src"]}\n'
for k in res['tgt']:
tgt_k = res['tgt'][k]
out_k = res['out'][k]
text_val += f'\t* {k} (tgt): {tgt_k}\n'
text_val += f'\t* {k} (out): {out_k}\n'
for i, res in enumerate(syn_gen_results_fw):
text_val += f'* sample (syn_fw) #{i}\n'
text_val += f'\t* src: {res["src"]}\n'
for k in res['tgt']:
tgt_k = res['tgt'][k]
out_k = res['out'][k]
text_val += f'\t* {k} (tgt): {tgt_k}\n'
text_val += f'\t* {k} (out): {out_k}\n'
for i, res in enumerate(syn_gen_results_bw):
text_val += f'* sample (syn_bw) #{i}\n'
text_val += f'\t* src: {res["src"]}\n'
for k in res['tgt']:
tgt_k = res['tgt'][k]
out_k = res['out'][k]
text_val += f'\t* {k} (tgt): {tgt_k}\n'
text_val += f'\t* {k} (out): {out_k}\n'
add_text_summary('Sample', value=text_val, step=global_step)
for epoch in range(args.max_epoch):
data_reader.start_epoch()
for batch in tqdm(data_reader.iterator(args.batch_size),
desc=f'Epoch {epoch}'):
# Train on real batch
real_loss = run_train_iter(batch)
# Train on synthetic batches
syn_batch_fw, syn_batch_bw = generate_synthetic_batch(batch)
syn_loss_fw = run_train_iter(syn_batch_fw)
syn_loss_bw = run_train_iter(syn_batch_bw)
global_step += 1
add_scalar_summary(name='real_loss', value=real_loss,
step=global_step)
add_scalar_summary(name='syn_loss_fw', value=syn_loss_fw,
step=global_step)
add_scalar_summary(name='syn_loss_bw', value=syn_loss_bw,
step=global_step)
if global_step % args.print_every == 0:
print_samples()
if global_step % args.save_every == 0:
model_filename = f'model-{global_step}.pt'
model_path = os.path.join(args.save_dir, model_filename)
torch.save(model.state_dict(), model_path)
print(f'\nIter #{global_step}: '
f'Saved checkpoint to {model_path}')
text, return_offsets_mapping=True, add_special_tokens=False)[
"offset_mapping"]
elif config["encoder"] == "BiLSTM":
tokenize = lambda text: text.split(" ")
def get_tok2char_span_map(text):
tokens = tokenize(text)
tok2char_span = []
char_num = 0
for tok in tokens:
tok2char_span.append((char_num, char_num + len(tok)))
char_num += len(tok) + 1 # +1: whitespace
return tok2char_span
preprocessor = Preprocessor(tokenize_func=tokenize,
get_tok2char_span_map_func=get_tok2char_span_map)
ori_format = config["ori_data_format"]
if ori_format != "tplinker": # if tplinker, skip transforming
for file_name, data in file_name2data.items():
if "train" in file_name:
data_type = "train"
if "valid" in file_name:
data_type = "valid"
if "test" in file_name:
data_type = "test"
data = preprocessor.transform_data(data,
ori_format=ori_format,
dataset_type=data_type,
add_id=True)
file_name2data[file_name] = data
from configuration import Config
from utils import CharaterTable, Preprocessor
from CaptionModel import CaptionModel
from tensorflow import flags
FLAGS = flags.FLAGS
flags.DEFINE_integer("caption_len", 25, "The length of caption")
flags.DEFINE_string(
"model_weights",
"/home/suxin/ImageCaption/ImageCaption_coding_test/checkpoint/weights.031-0.776.hdf5",
"The weights file of test model")
config = Config()
data = Preprocessor(config)
ctable = CharaterTable(data.train_captions + data.val_captions)
caption_len = FLAGS.caption_len
caption_model = CaptionModel(image_len=data.image_len,
caption_len=caption_len,
vocab_size=ctable.vocab_size,
ifpool=config.ifpool)
caption_model.build_inference_model(FLAGS.model_weights, beam_search=False)
result = caption_model.inference(data.val_set)
num = result.shape[0]
captions = [ctable.decode(result[i], calc_argmax=False) for i in range(num)]
for i, caption in enumerate(captions):
print i + 8001,
for word in caption:
from utils import save_model
from utils import loader
from featurizer import Featurize
from sentiment_analyzer import NaiveBayes
from sklearn.model_selection import train_test_split
from evaluator import evaluate_accuracy
import pickle
X, y = loader()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
print(X_train)
token = Preprocessor()
file1 = open('preprocessor.obj', 'wb')
pickle.dump(token, file1)
file1.close()
train_preprocessed = [token.tweet_cleaner(i) for i in X_train]
f = Featurize()
train_features = f.vectorize_train(train_preprocessed)
file2 = open('featurizer.obj', 'wb')
pickle.dump(f, file2)
file2.close()
model = NaiveBayes()
clf = model.train(train_features, labels=y_train)
save_model(clf)
print('Model is trained')
def cache_abstraction(env, params):
if os.path.exists('./out/{0}'.format(params['env_name'])):
shutil.rmtree('./out/{0}'.format(params['env_name']))
if params['use_preproc']:
preprocessor = Preprocessor(params['state_dim'], params['history'], params['use_luminance'],
params['resize_shape'])
params['state_dim'] = preprocessor.state_shape
else:
preprocessor = None
agent = VAE(params['state_dim'], params['action_dim'])
if params['use_cuda']:
agent = agent.cuda()
agent.load_state_dict(torch.load('./agents/{0}_{1}'.format(params['arch'], params['env_name'])))
else:
agent.load_state_dict(
torch.load('./agents/{0}_{1}'.format(params['arch'], params['env_name']), map_location='cpu'))
agent.eval()
agent_steps = 0
episode_rewards = []
start = time.time()
for episode in xrange(1):
env_state = env.reset()
episode_reward = 0.0
for t in xrange(1, params['max_steps'] + 1):
if params['env_render']:
env.render()
if preprocessor:
state = preprocessor.process_state(env_state)
else:
state = env_state
var_state = createVariable(state, use_cuda=params['use_cuda'])
# action, state_val = agent.sample_action_eval(var_state)
action, state_val, code = agent.sample_action_eval_code(var_state)
if not os.path.exists('./out/{0}/{1}'.format(params['env_name'], code)):
os.makedirs('./out/{0}/{1}'.format(params['env_name'], code))
preprocessor.get_img_state().save('./out/{0}/{1}/{2}.png'.format(params['env_name'], code, t))
reward = 0.0
for _ in range(1):
env_state, r, terminal, _ = env.step(action)
reward += r
if terminal:
break
episode_reward += reward
if terminal:
break
episode_rewards.append(episode_reward)
agent_steps += t
if preprocessor:
preprocessor.reset()
print 'Episode {0} | Total Steps {1} | Total Reward {2} | Mean Reward {3}' \
.format(episode, agent_steps, episode_reward, sum(episode_rewards[-100:]) / 100)
def eval_agent_parallel(envs, params):
preprocessors = []
for _ in range(params['num_envs']):
if params['use_preproc']:
preprocessor = Preprocessor(params['state_dim'], params['history'],
params['use_luminance'],
params['resize_shape'])
params['state_dim'] = preprocessor.state_shape
else:
preprocessor = None
preprocessors.append(preprocessor)
agent = agent_lookup(params)
restore_model(agent, params['restore'], params['use_cuda'])
if params['use_cuda']:
agent.cuda()
agent.eval()
episode_rewards = []
start = time.time()
for episode in xrange(1, params['num_episodes'] + 1):
env_states = [env.reset() for env in envs]
states = [
preprocessors[i].process_state(env_states[i])
if preprocessors[i] else env_states[i] for i in range(len(envs))
]
env_status = [False for _ in envs]
episode_reward = [0.0 for _ in envs]
for t in xrange(1, params['max_steps'] + 1):
if reduce(lambda x, y: x and y, env_status):
break
for i, env in enumerate(envs):
if params['env_render']:
env.render()
if env_status[i]:
continue
var_state = createVariable(states[i],
use_cuda=params['use_cuda'])
action, state_val = agent.sample_action_eval(var_state)
reward = 0.0
for _ in range(1):
env_states[i], r, terminal, _ = env.step(action)
reward += r
if terminal:
env_status[i] = True
break
#
episode_reward[i] += reward
states[i] = preprocessors[i].process_state(
env_states[i]) if preprocessors[i] else env_states[i]
for p in preprocessors:
p.reset()
episode_rewards.extend(episode_reward)
if episode % params['print_every'] == 0:
print 'Episode {0} | Total Reward {1} | Mean Reward {2} | Total Time {3} ' \
.format(episode, episode_reward, sum(episode_rewards[-100:]) / 100,
timeSince(start, episode / params['num_episodes']))
def cache_eval_episode(env, params):
cache_states, cache_distros = [], []
if params['use_preproc']:
preprocessor = Preprocessor(params['state_dim'], params['history'],
params['use_luminance'],
params['resize_shape'])
params['state_dim'] = preprocessor.state_shape
else:
preprocessor = None
agent = agent_lookup(params)
if params['use_cuda']:
agent = agent.cuda()
agent.load_state_dict(
torch.load('./agents/{0}_{1}'.format(params['arch'],
params['env_name'])))
else:
agent.load_state_dict(
torch.load('./agents/{0}_{1}'.format(params['arch'],
params['env_name']),
map_location='cpu'))
agent_steps = 0
episode_rewards = []
start = time.time()
for episode in xrange(1):
env_state = env.reset()
episode_reward = 0.0
for t in xrange(1, params['max_steps'] + 1):
if params['env_render']:
env.render()
if preprocessor:
state = preprocessor.process_state(env_state)
else:
state = env_state
var_state = createVariable(state, use_cuda=params['use_cuda'])
action, state_val, distro = agent.sample_action_distro(var_state)
cache_states.append(state)
cache_distros.append(distro.cpu().numpy())
reward = 0.0
for _ in range(1):
env_state, r, terminal, _ = env.step(action)
reward += r
if terminal:
break
episode_reward += reward
if terminal:
break
episode_rewards.append(episode_reward)
agent_steps += t
if preprocessor:
preprocessor.reset()
if episode % params['print_every'] == 0:
print 'Episode {0} | Total Steps {1} | Total Reward {2} | Mean Reward {3}' \
.format(episode, agent_steps, episode_reward, sum(episode_rewards[-100:]) / 100)
cache_states, cache_distros = np.array(cache_states), np.array(
cache_distros)
pickle.dump((cache_states, cache_distros),
open(
'./out/{0}_{1}_episode.pkl'.format(params['arch'],
params['env_name']),
'wb'), -1)
class TestStatsCalculator(unittest2.TestCase):
def setUp(self):
self.stats_calc = StatsCalculator()
self.preprocessor = Preprocessor()
self.col_names = [f'feature_{i}' for i in range(FEATURES)]
def tearDown(self):
self.stats_calc = None
self.preprocessor = None
def _get_df(self):
df = pd.read_csv('data/train.tsv', sep='\t')
df = self.preprocessor.split_features(df)
df = self.preprocessor.f_to_int(df)
return df
def test_mean_calc(self):
df = self._get_df()
col = random.choice(self.col_names)
res = self.stats_calc.calc_mean(df, col)
valid_res = np.mean(df[col])
self.assertEqual(res, valid_res, "Wrong mean calculation")
def test_std_calc(self):
df = self._get_df()
col = random.choice(self.col_names)
res = self.stats_calc.calc_std(df, col)
valid_res = np.std(df[col])
self.assertEqual(res, valid_res, "Wrong std calculation")
def test_speed(self):
"""
Test parallelized mean calculation
"""
df = self._get_df()
col = random.choice(self.col_names)
def wrapper(func):
def inner(df, col, multiproc=False):
start = time.time()
result = func(df, col)
end = time.time()
print(f'\nResult of calculation: {result}')
if multiproc:
print(f'Timing of calc in parallel: {end - start}')
else:
print(f'Timing of sequential calc: {end - start}')
return result
return inner
seq_calc = wrapper(self.stats_calc.calc_mean)
res = seq_calc(df, col)
parallel_calc = wrapper(self.stats_calc.calc_mean)
parallel_calc(df, col, multiproc=True)
true_value = np.mean(df[col])
self.assertEqual(res, true_value, "Wrong mean calculation")
def setUp(self):
self.stats_calc = StatsCalculator()
self.preprocessor = Preprocessor()
self.feature_adder = FeatureAdder()
self.col_names = [f'feature_{i}' for i in range(FEATURES)]
def train(**kwargs):
kwargs["real_batch_size"] = kwargs["batch_size"] * kwargs["gradient_accumulation"]
kwargs["train_dataset"] = kwargs["train_dataset"].split("-")
kwargs["validation_dataset"] = kwargs["validation_dataset"].split("-")
args = SimpleNamespace(**kwargs)
if args.dryrun:
os.environ['WANDB_MODE'] = 'dryrun'
run = wandb.init(project="SAPAUT-PAUSES", name=args.run_name)
if args.load:
if not os.path.exists(f"models/{args.load}"):
artifact = run.use_artifact(args.load + ":latest")
artifact.download(root=f"models/{args.load}")
args.model_name = f"models/{args.load}"
special_tokens = ["<punct>"]
if args.include_pauses:
if not args.replace_pause:
special_tokens.append("<pause>")
ds_type = "ref-pauses"
else:
ds_type = "ref"
if args.teacher_forcing:
ds_type += "-tf"
if args.tagging:
ds_type += "-tag"
if args.pause_threshold != 0.2:
download_mode="reuse_cache_if_exists"
else:
download_mode="reuse_dataset_if_exists"
ds_train = load_dataset(
f"punctuation-iwslt2011/{args.train_dataset[0]}.py",
ds_type,
download_mode=download_mode,
splits=[args.train_dataset[1]],
ignore_verifications=True,
lookahead_range=args.lookahead,
pause_threshold=args.pause_threshold,
)
print("len", len(ds_train["validation"]))
tokenizer = AutoTokenizer.from_pretrained(
args.model_name, fast=True, additional_special_tokens=special_tokens, add_prefix_space=args.tagging
)
if not args.tagging:
label_names = ds_train[args.train_dataset[1]].features["label"].names
else:
label_names = ds_train[args.train_dataset[1]].features["label"].feature.names
preprocessor = Preprocessor(
tokenizer,
args,
label_names,
args.replace_pause,
args.tagging,
)
ds_train = ds_train.map(
preprocessor.preprocess, batched=False, num_proc=args.num_proc
)
ds_train.rename_column_("label", "labels")
ds_valid = load_dataset(
f"punctuation-iwslt2011/{args.validation_dataset[0]}.py",
ds_type,
download_mode=download_mode,
splits=[args.validation_dataset[1]],
ignore_verifications=True,
lookahead_range=args.lookahead,
pause_threshold=args.pause_threshold,
)
ds_valid = ds_valid.map(
preprocessor.preprocess, batched=False, num_proc=args.num_proc
)
ds_valid.rename_column_("label", "labels")
train = ds_train[args.train_dataset[1]]
valid = ds_valid[args.validation_dataset[1]]
train.shuffle(42)
valid.shuffle(42)
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=args.epochs,
per_device_train_batch_size=args.batch_size,
per_device_eval_batch_size=args.batch_size,
weight_decay=args.weight_decay,
logging_dir="./logs",
logging_steps=args.log_steps,
evaluation_strategy="steps",
gradient_accumulation_steps=args.gradient_accumulation,
eval_steps=args.log_steps,
)
config = AutoConfig.from_pretrained(
args.model_name,
num_labels=4,
)
if not args.tagging:
model = AutoModelForSequenceClassification.from_pretrained(
args.model_name, config=config
)
else:
if args.bilstm:
model = RobertaBiLSTMForTokenClassification.from_pretrained(
args.model_name, config=config
)
else:
model = AutoModelForTokenClassification.from_pretrained(
args.model_name, config=config
)
model.resize_token_embeddings(len(tokenizer))
optimizer = AdamW(
[
{"params": model.base_model.parameters()},
{"params": model.classifier.parameters()},
],
lr=args.lr,
weight_decay=args.weight_decay,
)
if args.resample != "None":
function_dict = {
"Max": np.max,
"Mean": np.mean,
"Median": np.median,
}
np_function = function_dict[args.resample]
mean_samples_excl_none = int(
np_function(sorted(np.unique(train["labels"], return_counts=True)[1])[:-1])
)
per_class_samples = mean_samples_excl_none
balanced_filter = np.concatenate(
[
np.where(np.array(train["labels"]) == i)[0][:per_class_samples]
for i in range(4)
],
axis=0,
)
train = train.select(balanced_filter)
total_steps = len(train) // args.real_batch_size
total_steps = total_steps * args.epochs
schedule = get_linear_schedule_with_warmup(optimizer, total_steps // 2, total_steps)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train,
eval_dataset=valid,
compute_metrics=preprocessor.compute_metrics,
optimizers=(optimizer, schedule),
)
wandb.config.update(args.__dict__)
if not args.no_train:
trainer.train()
if args.tagging:
return
lookahead_test = []
for i in range(5):
lookahead_test.append(
valid.select(np.where(np.array(valid["lookahead"]) == i)[0])
)
la_metrics = []
for l_test in lookahead_test:
la_metrics.append(trainer.predict(l_test).metrics)
for k in la_metrics[0].keys():
data = [[i, m[k]] for i, m in enumerate(la_metrics)]
table = wandb.Table(data=data, columns=["lookahead", k])
wandb.log(
{
f"{k}_lookahead": wandb.plot.line(
table, "lookahead", k, title=f"{k} vs. lookahead"
)
}
)
if args.save:
trainer.save_model(f"models/{args.save}")
tokenizer.save_pretrained(f"models/{args.save}")
model_artifact = wandb.Artifact(args.save, type="model")
for path in glob.glob(f"models/{args.save}/**/*.*", recursive=True):
model_artifact.add_file(path)
wandb.run.log_artifact(model_artifact)
for i in range(5):
res_dict = {key: round(val * 100, 1) for key, val in la_metrics[i].items()}
print(f"------- {i} ----------")
print(
"COMMA",
res_dict["eval_precision_<comma>"],
res_dict["eval_recall_<comma>"],
res_dict["eval_f1_<comma>"],
)
print(
"PERIOD",
res_dict["eval_precision_<period>"],
res_dict["eval_recall_<period>"],
res_dict["eval_f1_<period>"],
)
print(
"QUESTION",
res_dict["eval_precision_<question>"],
res_dict["eval_recall_<question>"],
res_dict["eval_f1_<question>"],
)
print(
"OVERALL",
res_dict["eval_precision"],
res_dict["eval_recall"],
res_dict["eval_f1"],
)
print()
CSV_FILES = ["sentiment140_labeled_done.csv"]
# first read in the data
data = dict()
for file in TRAINING_FILES:
with open(os.path.join(TRAINING_DATA_PATH, file), "rb") as f:
data[file] = pickle.load(f, encoding='latin1')
for file in CSV_FILES:
data[file] = pd.read_csv(os.path.join(TRAINING_DATA_PATH, file),
encoding="ISO-8859-1",
header=0,
names=["id", "text", "labels"])
# second, preprocess the data for model ingestion
pp = Preprocessor(data, debug=False)
X_base_train, y_base_train, vocab_processor_base, X_base_test, y_base_test = pp.preprocess(
datasource="s140")
X1_train, y1_train, vocab_processor1, X1_test, y1_test = pp.preprocess(
datasource="scv1")
X2_train, y2_train, vocab_processor2, X2_test, y2_test = pp.preprocess(
datasource="scv2")
X_val, y_val = pp.preprocess(datasource="s140", split=False)
# third, run the models
# baseline
cnn_model_base = SarcasmCNN(data=((X_base_train, y_base_train), (X_base_test,
y_base_test)),
vocab_processor=vocab_processor_base)
print("Baseline Performance")
cnn_model_base.run()
def main(train_csv, model_dir, mode):
start_time = time.time()
#df = pd.read_csv(args.train_csv, low_memory = False)
df = pd.read_csv(train_csv)
is_big = df.memory_usage().sum() > BIG_DATASET_SIZE
# dict with data necessary to make predictions
model_config = {}
model_config['is_big'] = is_big
preprocessor = Preprocessor()
df_X, df_y = preprocessor.fit_transform(df)
model_config['features'] = preprocessor.features
print('Dataset read, shape {}'.format(df_X.shape))
# fitting
model_config['mode'] = mode
if mode == 'regression':
ridge_model = Ridge()
cb_model = cb.CatBoostRegressor(
iterations=300,
boosting_type=('Ordered' if len(df_X) < 1000 else 'Plain'),
od_type="IncToDec",
depth=6,
od_pval=0.0001,
#learning_rate=0.03,
loss_function='RMSE')
models = [ridge_model, cb_model]
else:
log_reg_model = LogisticRegression()
cb_model = cb.CatBoostClassifier(
iterations=300,
boosting_type=('Ordered' if len(df_X) < 1000 else 'Plain'),
od_type="IncToDec",
depth=6,
od_pval=0.0001,
#learning_rate=0.03,
loss_function='Logloss',
logging_level='Verbose')
models = [log_reg_model, cb_model]
for model in models:
model.fit(df_X, df_y)
D = [1 / np.std(model.predict(df_X) - df_y)**2 for model in models]
s = sum(D)
coef = [d / s for d in D]
model = Model(models, coef)
model_config['model'] = model
model_config_filename = os.path.join(model_dir, 'model_config.pkl')
with open(model_config_filename, 'wb') as fout:
pickle.dump(model_config, fout, protocol=pickle.HIGHEST_PROTOCOL)
print('Train time: {}'.format(time.time() - start_time))
import cv2
import numpy as np
from utils.Face_Detector import *
from utils.Preprocessor import *
from utils.Model_Loader import *
haar_face_detector = Haar_Face_Detector('./models/front_face_cascade.xml')
preprocessor = Preprocessor(96, 96)
model_loader = Model_Loader('./models/model_2.h5')
model_loader.load_model()
video_capture = cv2.VideoCapture(0)
video_capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
video_capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
while True:
_, frame = video_capture.read()
rois = haar_face_detector.detect(frame)
try:
x, y, w, h = rois[0]
roi = frame[y:y + h, x:x + w]
preprocessed_roi = preprocessor.process(roi)
eye_x, eye_y = model_loader.get_coordinates(preprocessed_roi,
preprocessed_roi.shape[0],
preprocessed_roi.shape[1])
# cv2.circle(preprocessed_roi, (int(eye_x[0]), int(eye_y[0])), 5, (0, 255, 0), -1)
