def train_test():
if os.path.isfile('pickle_train') and os.path.isfile('pickle_test'):
test = util.load_pickle('pickle_test')
train = util.load_pickle('pickle_train')
test.index = [i for i in range(len(test))]
train.index = [i for i in range(len(train))]
return train, test
else:
#Load of the dataset preprocessed before
dataset = util.load_pickle('pickle_data_discrete')
header = [
'acceleration_mean', 'acceleration_stdev', 'pitch1', 'pitch2',
'pitch3', 'roll1', 'roll2', 'roll3', 'classes',
'total_accel_sensor_1', 'total_accel_sensor_2',
'total_accel_sensor_4'
]
#write header in train and test csv
with open('train_dataset.csv', "w", newline='') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(header)
csvFile.close()
with open('test_dataset.csv', "w", newline='') as csvFile:
writer = csv.writer(csvFile)
writer.writerow(header)
csvFile.close()
total_test = dataset[0:0]
total_train = dataset[0:0]
for i in range(5):
#find in dataset one class at once
c = dataset.loc[dataset['classes'] == i]
#training and testing of one class: 80% training, 20% testing of each class
train, test = train_test_split(c, test_size=0.2)
total_test = total_test.append(test)
total_train = total_train.append(train)
#append results in two csv, train and test
with open('train_dataset.csv', 'a', newline='') as csvFile:
train.to_csv(csvFile, header=False, index=False)
csvFile.close()
with open('test_dataset.csv', 'a', newline='') as csvFile:
test.to_csv(csvFile, header=False, index=False)
csvFile.close()
#write train and test in a pickle
util.to_pickle(total_train, 'pickle_train')
util.to_pickle(total_test, 'pickle_test')
total_train.index = [i for i in range(len(total_train))]
total_test.index = [i for i in range(len(total_test))]
return total_train, total_test
def __init__(self, name_tagger, corpus, mwe=True): """ When initialized it will load all the taggers. They are: * UnigramTagger * BigramTagger * TrigramTagger If not possible it will create them, and save them. If Multi-Word Expressions are not allowed its necessary to split them and then use a UnigramTagger to be trained Args: name_tagger: root part of the name of the tagger, like cess_esp corpus: corpus that will train the tagger mwe: It can allow Multi-Word Expressions """ self.mwe = mwe if not mwe: name_tagger += '_' + NOMWE_TEXT #set the names of the taggers like: # cess_es_unigram.tagger, cess_es_bigram.tagger # or cess_es_nomwe_unigram.tagger, cess_es_nomwe_bigram.tagger complete_names = [name_tagger + '_' + x for x in N_GRAM_NAMES] # Try to load the taggers. try: for x in complete_names: utilities.load_pickle(x, TAGGER_EXTENSION, TAGGER_PATH).tag(['hola']) #If it not work create them except IOError: print "\n*** First-time use of", name_tagger, " taggers ***" print "Training taggers ..." timer = utilities.Timer() if self.mwe: cess_sents = corpus.tagged_sents() train_tagger(name_tagger, cess_sents) else: #Without mutliwords we need to split them cess_sents = unchunk(corpus.tagged_sents()) #We need the mwe tagger to train aux_tagger = tagger(name_tagger, corpus, mwe=True) tagged_cess_nomwe = aux_tagger.uni.tag_sents(cess_sents) train_tagger(name_tagger + '_' + NOMWE_TEXT, tagged_cess_nomwe) print "\nAll taggers trained in", timer.get_time(), "seconds" # Load tagger self.uni = utilities.load_pickle(complete_names[0], TAGGER_EXTENSION, TAGGER_PATH) self.bi = utilities.load_pickle(complete_names[1], TAGGER_EXTENSION, TAGGER_PATH) self.tri = utilities.load_pickle(complete_names[2], TAGGER_EXTENSION, TAGGER_PATH)
def sent_tokenize(text, language="spanish"):
"""
It splits the text into sentences
Args:
text: text to be splited
language: language of the tokenizer to be used
Returns:
List of sentences
"""
#try to use from local
try:
from utilities import load_pickle
tokenizer = load_pickle(language,
".pickle",
path="nltk_data/tokenizer/punkt/")
return tokenizer.tokenize(text)
#if not, use nltk
except IOError:
from nltk import sent_tokenize
return sent_tokenize(text, language)
def get_classifier(name, x_train=None, y_train=None, train=False):
"""
It will load the specified classifier. If it's not possible it will train if it has x_train and y_train
Args:
name: name of the classifier
x_train: metrics to train the classifier
y_train: labels to train the classifier
train: if true, it will force to train the classifier without loading it
Returns:
the classifier
"""
#if asked by user, train anyway
if train:
clf = train_classifier(name, x_train, y_train)
#if not, try to load previously processed data
else:
try:
clf = u.load_pickle(name, path=ML_path)
#if not found, try to train it
except IOError:
print "Pickle object not found, starting to train the classifier"
#Check if it is possible to train the classifier
if x_train is None or y_train is None:
print "\n\nNot possible to train the classifier without x_train and y_train\n\n"
else:
clf = train_classifier(name, x_train, y_train)
return clf
def tokenize_outputs(model_path, test_x, output_path):
RESOURCES_PATH = os.path.join(os.getcwd(), 'resources')
char2idx_path = os.path.join(RESOURCES_PATH, 'char2idx.pkl')
idx2label_path = os.path.join(RESOURCES_PATH, 'idx2label.pkl')
char2idx = load_pickle(char2idx_path)
idx2label = load_pickle(idx2label_path)
vocab_size = len(char2idx.items())
out_vocab_size = len(idx2label.items())
# init hyperparameters
hyperparams = HyperParameters()
hyperparams.vocab_size = vocab_size
hyperparams.num_classes = out_vocab_size
# Load model
model = BaselineModel(hyperparams)
try:
model.load_state_dict(torch.load(model_path))
except RuntimeError:
model.load_state_dict(
torch.load(model_path, map_location=torch.device('cpu')))
# compute predictions
y_pred = []
for data_x in tqdm(test_x, desc='Computing predictions'):
data_x_ = [char2idx.get(char, 1) for char in data_x]
data_x = torch.LongTensor(data_x_)
logits, _ = predict(model, data_x.unsqueeze(0))
pred_y = WikiDataset.decode_output(logits, idx2label)[0]
y_pred.append(pred_y)
# Save to text file
with open(output_path, encoding='utf-8', mode='w+') as outputs_file:
for prediction in tqdm(y_pred, desc='Writing predictions'):
outputs_file.write(f"{''.join(prediction)}\n")
def plot_all(folder, config=None):
# If no config object is given, load from selected folder
if config == None:
filename = glob.glob(folder + '/*.pkl')[-1]
config = load_pickle(filename)
plt.style.use(['seaborn-paper', 'seaborn-whitegrid'])
plt.rc("font", family="serif")
plt.rc('xtick', labelsize='x-small')
plt.rc('ytick', labelsize='x-small')
width = 4
height = width / 1.618
plt.close('all')
config.width = width
config.height = height
# Read data from folder
data_opt = fix_units(
pd.read_excel(glob.glob(folder + '/Intermediates/*.xlsx')[-1]))
data_ss = fix_units(pd.read_excel(glob.glob(folder + '/ss*.xlsx')[-1]))
# Set output folder
plot_folder = os.path.join(config.results_folder, 'Plots')
if not os.path.exists(plot_folder):
os.makedirs(plot_folder)
config.plot_folder = plot_folder
# Plot solar data
solar_plots(data_ss, config)
# Plot energy data
energy_plots(data_opt, data_ss, config)
# Plot 3D Trajectory
trajectory_plots_3D(data_opt, config)
# Plot miscelanseous plots
misc_plots(data_opt, config)
print("Extracting useful features from the dataset..")
dataset = features_extraction(dataset)
print('Dataset with extracted features')
print(dataset[:100])
#Sample dataset
print("Sampling dataset...")
dataset_sampled = sample_dataset(dataset)
print(dataset_sampled)
#Save dataframe as pickle
print('Saving dataset_sampled to pickle object...')
util.to_pickle(dataset_sampled, 'dataset_sampled')
print('\n Dataset sampled')
dataset_sampled = util.load_pickle('dataset_sampled')
print(dataset_sampled)
#Feature selection (Mark Hall's algorithm, reference paper)
print('Feature selection...')
dataset_discrete = feature_selection_discretization(dataset_sampled)
with open('discrete_dataset.csv', 'w', newline='') as c:
dataset_discrete.to_csv(c, header=True, index=False)
c.close()
print("Discrete dataset\n")
print(dataset_discrete)
#Save dataframe as pickle
print('Saving dataset_discrete to pickle object...')
def predict_multilingual(input_path: str, output_path: str, resources_path: str, lang: str) -> None:
"""
DO NOT MODIFY THE SIGNATURE!
This is the skeleton of the prediction function.
The predict function will build your model, load the weights from the checkpoint and write a new file (output_path)
with your predictions in the "<id> <BABELSynset>" format (e.g. "d000.s000.t000 bn:01234567n").
The resources folder should contain everything you need to make the predictions. It is the "resources" folder in your submission.
N.B. DO NOT HARD CODE PATHS IN HERE. Use resource_path instead, otherwise we will not be able to run the code.
If you don't know what HARD CODING means see: https://en.wikipedia.org/wiki/Hard_coding
:param input_path: the path of the input file to predict in the same format as Raganato's framework (XML files you downloaded).
:param output_path: the path of the output file (where you save your predictions)
:param resources_path: the path of the resources folder containing your model and stuff you might need.
:param lang: the language of the dataset specified in input_path, specify which model to load on which dataset
:return: None
"""
# load the model
model_path = os.path.join(resources_path, 'SensEmbed_BiLSTM_ATT_MultiTask_model.h5')
model = load_model(model_path, custom_objects={'SeqSelfAttention': SeqSelfAttention})
logging.info(f'{model._name} is loaded.')
# load tokenizer, fetch our vocabulary size
tokenizer_path = os.path.join(resources_path, 'multilingual_tokenizer.pkl')
tokenizer = load_pickle(tokenizer_path)
word_tokens = [word for word in tokenizer.word_index if 'bn:' not in word]
sense_tokens = [word for word in tokenizer.word_index if 'bn:' in word]
vocabulary_size = len(word_tokens)
output_size = vocabulary_size + len(sense_tokens)
batch_size = 8 # hard coded; as this was the one worked on Colab Google
# Parse the testing dataset
gold_dict_path = input_path.replace("data.xml", "gold.key.txt")
gold_dict = build_dict(gold_dict_path)
data_x, mask_x = parse_test(input_path, tokenizer=tokenizer, gold_dict=gold_dict, batch_size=batch_size)
# Getting the model predictions
predictions = []
for batch_x, batch_mask in tqdm(test_generator(np.array(data_x), batch_size, output_size,
use_elmo=False, mask_builder=np.array(mask_x),
tokenizer=tokenizer, use_bert=False),
desc="Predicting Senses"):
# Output Shape (batch_size, max_len_per_batch, output_vocab_size)
batch_pred = model.predict_on_batch([batch_x, batch_mask])
y_hat = np.argmax(batch_pred[0], axis=-1)
predictions.extend(y_hat)
# load lemma2synsets
lemma2synsets_file_path = os.path.join(os.getcwd(), 'resources', 'lemma2synsets4.0.xx.wn.ALL.txt')
lemma_synsets = get_lemma2synsets(lemma2synsets_file_path)
# load wordnet 2 babelnet synsets' mapping
bn2wn_path = os.path.join(resources_path, "babelnet2wordnet.tsv")
_, wordnet_babelnet_ = build_bn2wn_dict(bn2wn_path)
# Save predictions to a file
id_bn_list = []
# stands for predictions in {word_id babelnet_sense}
_predictions = []
for i, sentence in enumerate(tqdm(data_x, desc="Preparing models' predictions")):
for j, word in enumerate(sentence):
if len(mask_x[i][j]) == 2: # So it is an instance
prediction = predictions[i][j]
prediction_sense_ = tokenizer.index_word.get(prediction, '<OOV>')
if 'wn:' not in prediction_sense_ or 'bn:' not in prediction_sense_:
# Fallback Strategy
prediction_sense = predict_multilingual_sense(word=word, word2idx=tokenizer.word_index,
lemma_synsets=lemma_synsets,
wordnet_babelnet=wordnet_babelnet_)
else:
prediction_sense = prediction_sense_[prediction_sense_.find('bn:'):]
word_id = mask_x[i][j][1]
bn = prediction_sense if prediction_sense is not None else '<OOV>'
if word_id is None or bn is None:
continue
id_bn_list.append(f'{word_id}\t{bn}')
_predictions.append(bn)
# Writing model predictions
with open(output_path, encoding='utf-8', mode="w+") as output_file:
for id_bn in tqdm(id_bn_list, desc="Writing model predictions"):
output_file.write(f'{id_bn}\n')
# Fetching the ground truth of the data
ground_truth = []
ground_truth_path = input_path.replace("data.xml", "gold.key.txt")
with open(ground_truth_path, encoding='utf-8', mode='r') as ground_truth_file:
lines = ground_truth_file.read().splitlines()
for line in lines:
sense_key = line.split()[1]
ground_truth.append(sense_key)
# Compute F1_Score
_, _, f1score, _ = precision_recall_fscore_support(ground_truth, _predictions, average='micro')
print(f'{model._name} F1_score: {f1score}')
def predict_lexicographer(input_path: str, output_path: str,
resources_path: str):
"""
DO NOT MODIFY THE SIGNATURE!
This is the skeleton of the prediction function.
The predict function will build your model, load the weights from the checkpoint and write a new file (output_path)
with your predictions in the "<id> <lexicographerId>" format (e.g. "d000.s000.t000 noun.animal").
The resources folder should contain everything you need to make the predictions. It is the "resources" folder in your submission.
N.B. DO NOT HARD CODE PATHS IN HERE. Use resource_path instead, otherwise we will not be able to run the code.
If you don't know what HARD CODING means see: https://en.wikipedia.org/wiki/Hard_coding
:param input_path: the path of the input file to predict in the same format as Raganato's framework (XML files you downloaded).
:param output_path: the path of the output file (where you save your predictions)
:param resources_path: the path of the resources folder containing your model and stuff you might need.
:return: None
"""
# Load configuration params
config_path = os.path.join(os.getcwd(), 'config.yaml')
config_file = open(config_path)
config_params = yaml.load(config_file)
# Parse and process Test set
elmo = config_params["use_elmo"]
batch_size = config_params["batch_size"]
tokenizer_path = os.path.join(resources_path, 'tokenizer.pkl')
tokenizer = load_pickle(tokenizer_path)
data_x, test_mask_builder = parse_test(input_path, tokenizer, batch_size,
elmo) # Raw Data
word_tokens = [
word for word in tokenizer.word_index if not word.startswith('wn:')
]
sense_tokens = [
word for word in tokenizer.word_index if word.startswith('wn:')
]
vocabulary_size = len(word_tokens)
output_size = vocabulary_size + len(sense_tokens)
# Load model
model_path = os.path.join(resources_path, 'Baseline_model.h5')
custom_objs = {'ElmoEmbeddingLayer': ElmoEmbeddingLayer}
model = load_model(model_path, custom_objects=custom_objs)
logging.info(f'{model._name} is loaded.')
# Model Predictions
predictions = []
for batch_x, batch_test in tqdm(test_generator(np.array(data_x),
batch_size, output_size,
elmo,
np.array(test_mask_builder),
tokenizer, False),
desc="Predicting_Lexes"):
# Output Shape (batch_size, max_len_per_batch, output_vocab_size)
batch_pred = model.predict_on_batch([batch_x, batch_test])
# Output Shape (batch_size, max_len_per_batch)
y_hat = np.argmax(batch_pred, axis=-1)
predictions.extend(y_hat)
# Dictionaries to get mappings
bn2wn_path = os.path.join(resources_path, 'babelnet2wordnet.tsv')
_, wordnet_babelnet = build_bn2wn_dict(bn2wn_path)
bn2lex_path = os.path.join(resources_path, 'babelnet2lexnames.tsv')
babelnet_lex, _ = build_bn2lex_dict(bn2lex_path)
# Save predictions to a file
id_lex_list = [] # stands for predictions in {word_id babelnet_sense}
for i, sentence in enumerate(
tqdm(data_x, desc='Formatting model predictions into ID_LEX')):
for j, word in enumerate(sentence):
if len(test_mask_builder[i][j]) == 2: # So it is an instance
prediction = predictions[i][j]
prediction_sense = tokenizer.index_word.get(
prediction, '<OOV>')
if 'wn:' not in prediction_sense or 'bn:' not in prediction_sense:
prediction_sense = predict_sense(token=word) # Fallback
word_id = test_mask_builder[i][j][1]
id_ = word_id[word_id.find('.') + 1:]
bn = wordnet_babelnet.get(prediction_sense, None)
lex = babelnet_lex.get(bn, 'factotum')
if id_ is None or lex is None:
continue
id_lex_list.append(f'{id_}\t{lex}')
with open(output_path, mode="w+") as output_file:
for id_lex in tqdm(id_lex_list, desc="Writing model predictions"):
output_file.write(f'{id_lex}\n')
'Stacked_BiLSTM_CRF_Fasttext_2315.pth')
configure_workspace(seed=1873337)
train_dataset, dev_dataset, test_dataset = prepare_data(CRF_MODEL)
train_dataset_ = DataLoader(dataset=train_dataset, batch_size=batch_size)
dev_dataset_ = DataLoader(dataset=dev_dataset, batch_size=batch_size)
test_dataset_ = DataLoader(dataset=test_dataset, batch_size=batch_size)
embeddings_path = os.path.join(RESOURCES_PATH, 'wiki.en.vec')
pretrained_embeddings = load_pretrained_embeddings(
embeddings_path, train_dataset.word2idx, 300,
is_crf=CRF_MODEL) if PRETRAINED else None
idx2label = load_pickle(
os.path.join(RESOURCES_PATH,
'Stacked_BiLSTM_CRF_Fasttext_2315_idx2label.pkl'))
word2idx = load_pickle(
os.path.join(RESOURCES_PATH,
'Stacked_BiLSTM_CRF_Fasttext_2315_word2idx.pkl'))
hp = HyperParameters(name_, word2idx, train_dataset.idx2label,
pretrained_embeddings, batch_size)
model = CRF_Model(hp).to(
train_dataset.get_device) if CRF_MODEL else BaselineModel(hp).to(
train_dataset.get_device)
model.load_model(model_path)
evaluator = Evaluator(model, test_dataset_, CRF_MODEL)
evaluator.check_performance(idx2label)
tokens = test_dataset.data_x
if save_to is not None:
plt.savefig(f'{save_to}_acc.png')
plt.show()
def visualize_plot_mdl(visualize, plot, model):
# Balabizo acts as placeholder
name = model._name if model._name is not None else 'Balabizo'
if visualize:
print(f'\n{name} Model Summary: \n')
model.summary()
if plot:
img_path = os.path.join(os.getcwd(), 'resources', f'{name}_Model.png')
plot_model(model, to_file=img_path, show_shapes=True)
logging.info(f"{name} model image saved")
logging.info(f"{name} model is created & compiled")
def train_model_huge_dataset(dir_path, model):
# TODO: LOOP ON PICKLE FILES, LOAD THEM, TRAIN MODEL, COMPUTE F1 SCORE (ITERATIVELY)
# TODO: READ http://nlpprogress.com/english/word_sense_disambiguation.html
# TODO: READ ABOUT REGULARIZERS IN LSTMs
pass
if __name__ == '__main__':
history_path = os.path.join(
os.getcwd(), 'resources', 'SensEmbed_BiLSTM_ATT_MultiTask_history.pkl')
hist = load_pickle(history_path)
plot_history(hist)