def train_intent_model(int_ds, random_selection: bool, limit_num_sents: bool, num_samples=None):
if random_selection:
selection = get_intents_selection(int_ds['train'],
num_samples) # selected intent labels: (num_samples, ) np.ndarray
filt_train = get_filtered_lst(int_ds['train'],
selection) # almost the same as int_ds['train'] but filtered according to selection
filt_test = get_filtered_lst(int_ds['test'], selection)
mod_int_ds = copy.deepcopy(int_ds) # deepcopy in order to not modify the original dict
mod_int_ds['train'] = filt_train
mod_int_ds['test'] = filt_test
dataset = mod_int_ds
else:
dataset = int_ds
split = Split()
X_int_train, y_int_train = split.get_X_y(dataset['train'], fit=True, limit_num_sents=limit_num_sents,
set_type='train') # fit only on first dataset
X_int_test, y_int_test = split.get_X_y(dataset['test'] + dataset['oos_test'], fit=False,
limit_num_sents=limit_num_sents, set_type='test')
svc_int = svm.SVC(C=1, kernel='linear').fit(X_int_train, y_int_train)
return svc_int, X_int_test, y_int_test, split
def train_intent_model(int_ds,
random_selection: bool,
dim: int,
limit_num_sents: bool,
num_samples=None):
if random_selection:
selection = get_intents_selection(
int_ds['train'],
num_samples) # selected intent labels: (num_samples, ) np.ndarray
filt_train = get_filtered_lst(
int_ds['train'], selection
) # almost the same as int_ds['train'] but filtered according to selection
mod_int_ds = copy.deepcopy(
int_ds) # deepcopy in order to not modify the original dict
mod_int_ds['train'] = filt_train
dataset = mod_int_ds
else:
dataset = int_ds
train_str_int = dataset_2_string(dataset['train'],
limit_num_sents=limit_num_sents,
set_type='train')
X_int_test, y_int_test = get_X_y_fasttext(dataset['test'] +
dataset['oos_test'],
limit_num_sents=limit_num_sents,
set_type='test')
with NamedTemporaryFile() as f:
f.write(train_str_int.encode('utf8'))
f.seek(0)
# Train model for in-scope queries
model_int = fasttext.train_supervised(
input=f.name,
dim=dim,
pretrainedVectors=f'{PRETRAINED_VECTORS_PATH}/cc.en.{dim}.vec')
return model_int, X_int_test, y_int_test
def train_intent_model(int_ds,
random_selection: bool,
limit_num_sents: bool,
num_samples=None):
if random_selection:
selection = get_intents_selection(
int_ds['train'],
num_samples) # selected intent labels: (num_samples, ) np.ndarray
filt_train = get_filtered_lst(
int_ds['train'], selection
) # almost the same as int_ds['train'] but filtered according to selection
mod_int_ds = copy.deepcopy(
int_ds) # deepcopy in order to not modify the original dict
mod_int_ds['train'] = filt_train
dataset = mod_int_ds
else:
dataset = int_ds
train_str_int = dataset_2_string_rasa(dataset['train'],
limit_num_sents=limit_num_sents,
set_type='train')
X_int_test, y_int_test = get_X_y_rasa(dataset['test'] +
dataset['oos_test'],
limit_num_sents=limit_num_sents,
set_type='test')
with NamedTemporaryFile(suffix='.yml') as f:
f.write(train_str_int.encode('utf8'))
f.seek(0)
training_data = rasa.shared.nlu.training_data.loading.load_data(f.name)
config = rasa.nlu.config.load('config.yml')
trainer = rasa.nlu.model.Trainer(config)
model_int = trainer.train(training_data)
return model_int, X_int_test, y_int_test
def train_intent_model(int_ds,
random_selection: bool,
limit_num_sents: bool,
num_samples=None):
if random_selection:
selection = get_intents_selection(
int_ds['train'],
num_samples) # selected intent labels: (num_samples, ) np.ndarray
filt_train = get_filtered_lst(
int_ds['train'], selection
) # almost the same as int_ds['train'] but filtered according to selection
filt_test = get_filtered_lst(int_ds['test'], selection)
mod_int_ds = copy.deepcopy(
int_ds) # deepcopy in order to not modify the original dict
mod_int_ds['train'] = filt_train
mod_int_ds['test'] = filt_test
dataset = mod_int_ds
else:
dataset = int_ds
# Split and tokenize dataset
split = Split_BERT()
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
X_int_train, y_int_train = split.get_X_y(dataset['train'],
limit_num_sents=limit_num_sents,
set_type='train')
X_int_val, y_int_val = split.get_X_y(dataset['val'],
limit_num_sents=limit_num_sents,
set_type='val')
X_int_test, y_int_test = split.get_X_y(dataset['test'] +
dataset['oos_test'],
limit_num_sents=limit_num_sents,
set_type='test')
train_int_ids, train_int_attention_masks, train_int_labels = tokenize_BERT(
X_int_train, y_int_train, tokenizer)
val_int_ids, val_int_attention_masks, val_int_labels = tokenize_BERT(
X_int_val, y_int_val, tokenizer)
num_labels = len(split.intents_dct.keys(
)) - 1 # minus 1 because 'oos' label isn't used in training
# Train model
model_int = TFBertForSequenceClassification.from_pretrained(
'bert-base-uncased',
num_labels=num_labels) # we have to adjust the number of labels
print('\nBert Model', model_int.summary())
log_dir = 'tensorboard_data/tb_bert'
model_save_path = './models/bert_model.h5'
callbacks = [
tf.keras.callbacks.ModelCheckpoint(filepath=model_save_path,
save_weights_only=True,
monitor='val_loss',
mode='min',
save_best_only=True),
tf.keras.callbacks.TensorBoard(log_dir=log_dir)
]
loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metric = tf.keras.metrics.SparseCategoricalAccuracy('accuracy')
optimizer = tf.keras.optimizers.Adam(learning_rate=4e-5)
model_int.compile(loss=loss, optimizer=optimizer, metrics=[metric])
history = model_int.fit(
[train_int_ids, train_int_attention_masks],
train_int_labels,
batch_size=32,
epochs=5,
validation_data=([val_int_ids,
val_int_attention_masks], val_int_labels),
callbacks=callbacks)
return model_int, X_int_test, y_int_test, split
results_dct = evaluate(int_ds, LIMIT_NUM_SENTS)
print_results(dataset_size, results_dct)
else:
for num_samples in [3, 6, 9,
12]: # choose only a certain number of samples
print(
f'{repetitions} times random selection {num_samples} intents'
)
accuracy_lst, recall_lst = [], []
far_lst, frr_lst = [], []
for i in range(repetitions):
selection = get_intents_selection(
int_ds['train'], num_samples
) # selected intent labels: (num_samples, ) np.ndarray
filt_train = get_filtered_lst(
int_ds['train'], selection
) # almost the same as int_ds['train'] but filtered according to selection
filt_test = get_filtered_lst(int_ds['test'], selection)
mod_int_ds = copy.deepcopy(
int_ds
) # deepcopy in order to not modify the original dict
mod_int_ds['train'] = filt_train
mod_int_ds['test'] = filt_test
temp_res = evaluate(mod_int_ds,
LIMIT_NUM_SENTS) # temporary results