def train(config, Model):
# Load the data and create X and Y matrices
data = get_data(config)
num_features = data.shape[1] - 1
X = data[:, :num_features]
Y = data[:, -1]
# split the data into training and test set
X_train, Y_train, X_test, Y_test = split_data(X,
Y,
0.80,
balance_dist=True)
X_train = np.expand_dims(X_train, axis=2)
X_test = np.expand_dims(X_test, axis=2)
Y_train = to_categorical(Y_train)
Y_test = to_categorical(Y_test)
# instantiate the CNN model and train on the data
model = Model(num_features, Y_train.shape[1])
history = model.fit(X_train,
Y_train,
batch_size=128,
epochs=100,
verbose=2)
# Evaluate the trained model on test data and print the accuracy
score = model.model.evaluate(X_test, Y_test)
print("\nTest accuracy: ", round(score[1] * 100, 2))
print("Test loss: ", round(score[0], 2))
return history
def main():
import config
from model import load_model
model = load_model()
while not model:
config.model_path = input('valid model: ')
model = load_model()
from data import load_data, split_data
d = load_data(with_meta=True)
d, _ = split_data(d)
# from random import shuffle
# shuffle(d)
d = d[:config.hm_output_file]
for i, (seq, meta) in enumerate(d):
from model import respond_to
_, seq = respond_to(model, [seq[:config.hm_extra_steps]],
training_run=False,
extra_steps=config.hm_extra_steps)
seq = seq.detach()
if config.use_gpu:
seq = seq.cpu()
seq = seq.numpy()
from data import data_to_audio, write
seq = data_to_audio(seq, meta)
write(f'{config.output_file}{i}.wav', config.sample_rate, seq)
def main():
# Generate and split data
# Try and play with arguments
all_data = data.generate_data_gauss(numSamples=1000, noise=0.5)
train_data, valid_data = data.split_data(all_data, val_factor=0.3)
# Set show to True if you want to see generated dataset
data.plot_data(train_data, valid_data, show=False)
# Directory to save summaries to
# From your conda environment run
# tensorbard --logdir ../tf_playground/output
# to see training details
output = utils.get_output_dir()
# Create model
# Go to model.py file to make changes to the model
model = Model()
# Lets train
# Try changing number of epochs and batch_size
trainer = Trainer(train_data=train_data,
valid_data=valid_data,
model=model,
epochs=10,
batch_size=2,
output=output)
trainer.train()
trainer.save_final_accuracy()
def main(unused_argv):
if len(unused_argv) != 1: # prints a message if you've entered flags incorrectly
raise Exception('Problem with flags: %s' % unused_argv)
# choose what level of logging you want
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
# user_rating has the elements in the following order
# user_id, item_id, rating, time, num_words, review
user_rating, user_id_to_idx, item_id_to_idx = read_file(FLAGS.data_path)
num_users = len(user_id_to_idx)
num_items = len(item_id_to_idx)
num_reviews = len(user_rating)
print('Number of total users / items / reviews: %d / %d / %d' %
(num_users, num_items, num_reviews))
users_ratings = [ur for ur in user_rating]
train_ratings, test_ratings, valid_ratings = split_data(users_ratings)
# build vocabulary
id_to_word, word_to_id = build_vocab(users_ratings, FLAGS.vocab_size)
train_item_doc = token_to_id(train_ratings, word_to_id)
valid_item_doc = token_to_id(valid_ratings, word_to_id)
current_datetime = datetime.now()
subfolder_timestamp = datetime.strftime(current_datetime, '%Y%m%d-%H%M%S')
subfolder_dataname = os.path.basename(FLAGS.data_path)
log_folder = os.path.join(FLAGS.log_root, subfolder_dataname + '-' + subfolder_timestamp)
# save vocab to output folder
pathlib.Path(log_folder).mkdir(parents=True, exist_ok=True)
with open(os.path.join(log_folder, 'vocab.csv'), 'w') as f:
for idx, token in id_to_word.items():
f.write('%s,%s\n' % (idx, token))
# Try offset model
offset_model = offsetModel(train_ratings, valid_ratings, test_ratings)
offset_model.train()
# Make a namedtuple hps, containing the values of the hyperparameters that the model needs
hparam_list = ['init_stddev', 'emb_dim', 'min_kappa', 'max_kappa',
'vocab_size', 'mu', 'max_iter_steps', 'num_iter_steps',
'threshold']
hps_dict = {}
for key,val in FLAGS.flag_values_dict().items(): # for each flag
if key in hparam_list: # if it's in the list
hps_dict[key] = val # add it to the dict
hps = namedtuple('HParams', hps_dict.keys())(**hps_dict)
hft_model = HFTModel(hps, train_ratings, valid_ratings, test_ratings,
train_item_doc, valid_item_doc,
num_users, num_items, num_reviews, log_folder)
hft_model.build_graph()
hft_model.train()
def test_precision(self):
df = pd.read_pickle('../data/final/df_final.pkl')
data = d.split_data(df, True)
data_train = data[0]
data_test = data[1]
data_val = data[2]
b = base.baseline(df, False)
als_result = als_precision(data_train, data_val, b)
assert 1 == 1
def main(disp_text=True):
if config.fresh_model:
config.all_losses = []
save_model(make_model())
model = load_model()
if disp_text: print('created model.', end=' ')
else:
model = load_model()
if not model:
save_model(make_model())
model = load_model()
if disp_text: print('created model.', end=' ')
else:
if disp_text: print('loaded model.', end=' ')
data = load_data()
data, data_dev = split_data(data)
data = [d for i,d in enumerate(data) if i in [8,10,13,14]]
print()
seq_lens = [len(d) for d in data]
print(f'seq lens: {seq_lens}')
min_seq_len = min(seq_lens)
print(f'min seq len: {min_seq_len}')
if not config.max_seq_len or config.max_seq_len > min_seq_len:
config.max_seq_len = min_seq_len
data = [d[:config.max_seq_len] for d in data]
# from random import choice
# from torch import randn
# data = [[randn(config.in_size) for _ in range(choice(range(config.max_seq_len//2,config.max_seq_len)))] for _ in range(10)]
# data_dev = []
# for d in data: print(len(d))
if not config.batch_size or config.batch_size >= len(data):
config.batch_size = len(data)
elif config.batch_size < 1:
config.batch_size = int(len(data)*config.batch_size)
if disp_text: print(f'hm data: {len(data)}, hm dev: {len(data_dev)}, bs: {config.batch_size}, lr: {config.learning_rate}, \ntraining started @ {now()}')
for ep in range(config.hm_epochs):
for i, batch in enumerate(batchify_data(data)):
train_on(model, batch)
return model
def load_data(paths, report=sys.stdout, **kwargs):
import data
dtypes = [parse_type(path) for path in paths]
if not all(t == dtypes[0] for t in dtypes[1:]):
print >>report, "Error: all files must be of same type but were {}".format(dtypes)
raise Exception()
dtype = dtypes[0]
labels = dtype == labeled
Xt, Yt, Xv, Yv = data.split_data(read_files(paths,report=report,**kwargs), labels=labels
, report=sys.stdout, **kwargs)
if labels:
labels = max(np.max(Yt), np.max(Yv))+1
else:
labels = 0
return Xt, Yt, Xv, Yv, labels
def main(args):
x, fx = get_data(args)
device = torch.device("cuda" if args.cuda else "cpu")
train_data, val_data = split_data(args, x, fx)
if args.save_splits:
save_splits(train_data, val_data)
train_loader, val_loader = get_loaders(train_data, val_data)
model = get_model(args)
trainer = get_trainer(model, train_loader, val_loader, device, args)
trainer.train()
def main():
import config
from model import load_model
model = load_model(config.model_path + '_final')
while not model:
config.model_path = input('valid model: ')
model = load_model()
from data import load_data, split_data
d = load_data()
d, _ = split_data(d)
# from random import shuffle
# shuffle(d)
#d = d[:config.hm_output_file]
d = [d[8]] # [8,10,13,14]]
config.polyphony = True
for i, seq in enumerate(d):
from model import respond_to
seq = respond_to(model, seq[:1])
seq = [t.detach() for t in seq]
if config.use_gpu:
seq = [t.cpu() for t in seq]
seq = [t.numpy() for t in seq]
from data import note_reverse_dict, convert_to_midi
seq_converted = []
for timestep in seq:
if config.act_fn == 't': timestep = (timestep + 1) / 2
if config.polyphony:
t_converted = ''
for i, e in enumerate(timestep[0]):
if e > config.pick_threshold:
t_converted += note_reverse_dict[i % 12] + str(
int(i / 12) + config.min_octave
) if i != config.out_size - 1 else 'R'
t_converted += ','
t_converted = t_converted[:-1] if len(t_converted) else 'R'
else:
i = timestep[0].argmax()
t_converted = note_reverse_dict[i % 12] + str(
int(i / 12) + config.min_octave)
seq_converted.append(t_converted)
convert_to_midi(seq_converted).show()
def get_stats():
config = {
'unknown_freq': 2,
'gold_ratio': 0.1,
'inc_option': 'auxiliary',
'auxiliary_option': 'detection',
'seed': 66
}
dir_path = '/path/to/working/dir'
set_random_seed(config['seed'])
train_file = dir_path + '/data/ontonotes.development.ner'
print('load data')
train_data = get_data(train_file)
gold_data, inc_data = split_data(train_data, config)
print('get vocabulary')
word_to_ix, pos_to_ix, ner_to_ix = get_vocabulary(train_data, config)
config['ner_to_ix'] = ner_to_ix
config['pos_to_ix'] = pos_to_ix
config['word_to_ix'] = word_to_ix
config['output_size'] = len(ner_to_ix)
print('ner_to_ix', ner_to_ix)
print('word_to_ix', len(word_to_ix))
print('process data')
inc_input_ids, inc_sent_ids, inc_pos_ids, inc_ner_ids = process_data(
inc_data, word_to_ix, pos_to_ix, ner_to_ix)
inc_ner_ids = get_incidental_data(inc_sent_ids, inc_input_ids, inc_pos_ids,
inc_ner_ids, config)
inc_label_counter = Counter()
for label in inc_ner_ids:
# if label[0] == 'B' or label[0] == 'I':
# label = label[2:]
inc_label_counter[label] += 1 / len(inc_ner_ids)
print('inc label counter', inc_label_counter)
inputs, sent_ids, pos_labels, ner_labels = inc_data
word_seqs = generate_sent_seqs(inputs, sent_ids)
pos_seqs = generate_sent_seqs(pos_labels, sent_ids)
ner_seqs = generate_sent_seqs(ner_labels, sent_ids)
inc_data = []
sent_counter = Counter()
for x in range(len(word_seqs)):
inc_data.append((word_seqs[x], pos_seqs[x], ner_seqs[x]))
sent_counter[len(word_seqs[x])] += 1 / len(word_seqs)
print('average sent length', len(sent_ids) / len(word_seqs))
print('sent length distribution', sent_counter.items())
def main(images_path, labels_path):
keras.backend.clear_session()
data_df = get_data(images_path, labels_path)
raw_train, valid = split_data(data_df)
model = create_model(num_classes=28, input_shape=input_shape)
model.compile(loss="binary_crossentropy",
optimizer=Adam(),
metrics=["acc", f1])
# model.compile(loss=[_focal_loss(gamma=2,alpha=0.75)], optimizer=Adam(), metrics=["acc", f1])
epochs = 50
batch_size = 64
checkpointer = ModelCheckpoint("../working/InceptionResNetV2.model",
verbose=2,
save_best_only=True)
early_stopping = EarlyStopping(monitor="val_loss", patience=2)
reduce_lr = ReduceLROnPlateau(monitor="val_loss", patience=1, factor=0.1)
train_generator = DataGenerator.create_train(raw_train,
batch_size,
DEFAULT_IMG_SIZE_WHC,
augument=True)
validation_generator = DataGenerator.create_train(valid,
100,
DEFAULT_IMG_SIZE_WHC,
augument=False)
train_steps = raw_train.shape[0] // batch_size
valid_steps = valid.shape[0] // batch_size
# train model
history = model.fit_generator(
train_generator,
steps_per_epoch=train_steps,
validation_data=next(validation_generator),
validation_steps=valid_steps,
epochs=epochs,
verbose=1,
callbacks=[checkpointer, reduce_lr],
)
def main():
try:
feature1 = request.form["feature1"]
feature2 = request.form["feature2"]
classifier = request.form["classifier"]
except KeyError:
error = "Warning! Missing selections. Please select two features from the dataset, and one classifier!"
return render_template('select.html', error=error)
df = read_diabetes()
x_train, x_test, y_train, y_test = split_data(df)
x_train, x_test = select_features(x_train, x_test, [feature1, feature2])
clf = eval(classifier + "()")
clf.fit(x_train, y_train)
plot_data = build_plot(clf, x_test, y_test)
accuracy = clf.score(x_test, y_test)
return render_template('plot.html', accuracy=accuracy, plot_url=plot_data)
def preproc_data():
from data import split_data
split_data('../data/hin-eng/hin.txt', '../data/hin-eng')
def CNN(X_train, X_test, y_train, y_test):
X_train, X_test, y_train, y_test = reshape_data(X_train, X_test, y_train,
y_test)
model = Sequential()
model.add(
Conv2D(64, kernel_size=3, activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(32, kernel_size=3, activation='relu'))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
validation_data=(X_test, y_test),
epochs=10,
batch_size=100)
plt.plot(history.history['loss'])
# plt.plot(history.history['val_loss'])
plt.title('Loss Function')
plt.ylabel('loss')
plt.xlabel('epoch')
# plt.legend(['train', 'test'], loc='upper left')
plt.show()
if __name__ == "__main__":
train_x, test_x, train_y, test_y = split_data()
CNN(train_x, test_x, train_y, test_y)
output, hidden = model(input, hidden)
output = output.squeeze()
output = softmax(output, dim=0)
p = output[current_idx].data # 概率
total_p += math.log(p) #e为底
return math.exp(-total_p * (1 / sentence_len))
def evaluate(model, test_dataset, dict):
ppl = 0
for sentence in test_dataset:
ppl += evaluate_iter(model, sentence, dict)
ppl = ppl / len(test_dataset)
print("evaluation ppl:", ppl)
return ppl
if __name__ == '__main__':
dataset = data.get_dataset(file_path)
dict = data.build_dict(dataset)
config.vocab_size = len(dict)
train_dataset, test_dataset = data.split_data(
dataset, train_proportion=config.train_proportion)
train_tokens = data.tokenize(train_dataset, dict)
model = RNNModel(config)
train_batch_source = data.batchify(train_tokens,
config.batch_size) #传入batchify好的数据直接训练
train(model, batch_source=train_batch_source)
#test
evaluate(model, test_dataset, dict)
def train(gpu: int, args: Namespace):
"""Implements the training loop for PyTorch a model.
Args:
gpu: the GPU device
args: user defined arguments
"""
# setup process groups
rank = args.nr * args.gpus + gpu
setup(rank, args)
# define the model
model = ResNext().architecture
model.cuda(gpu)
# Wrap the model
model = DDP(model, device_ids=[gpu])
# define loss function (criterion) and optimizer
criterion = nn.BCEWithLogitsLoss()
optimizer = Adam(model.parameters(), args.lr)
# split data
train_df = split_data(args.folds)
for fold in range(args.folds):
losses = []
scores = []
train_loader, valid_loader = get_data(args, train_df, fold, rank)
if gpu == 0:
print(f"Training started using fold {fold} for validation")
# train
model.train()
for epoch in range(args.epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.cuda(gpu)
labels = labels.cuda(gpu)
output = model(images)
loss = criterion(output, labels)
loss.backward()
optimizer.step()
optimizer.zero_grad()
if i % args.log_interval == 0 and gpu == 0:
print("Train Epoch: {} [{}/{} ({:.0f}%)]\tloss={:.4f}".format(
epoch+1, i, len(train_loader),
100. * i / len(train_loader), loss.item()))
# evaluate
model.eval()
with torch.no_grad():
for i, (images, labels) in enumerate(valid_loader):
images = images.cuda(gpu)
labels = labels.cuda(gpu)
output = model(images)
loss = criterion(output, labels).item()
score = get_score(labels.detach().cpu(), output.detach().cpu())
losses.append(loss)
scores.append(score)
if gpu == 0:
print("Validation loss={:.4f}\tAUC score={:.4f}".format(
statistics.mean(losses), statistics.mean(scores)))
# checkpoint model
model = checkpoint(model, gpu, fold)
if args.save_model and gpu == 0:
torch.save(model.module.state_dict(), "model.pt")
cleanup()
from data import get_train_data, get_vocab, split_data, response_len, post_len, padding
import random
import os
from pprint import pprint
import numpy as np
import time
id2w, w2id, freq = get_vocab()
from emo_cls.classification import Classification
from seq2seq_attention_9emo import Seq2SeqAttentionMinDis, Seq2SeqAttentionMaxDis, Seq2SeqAttentionEmoContent
from seq2seq_attention_9emo import Seq2SeqAttentionHappy, Seq2SeqAttentionSad, Seq2SeqAttentionAnger, Seq2SeqAttentionDisgust
from seq2seq_attention_9emo import Seq2SeqAttentionLike #,Seq2SeqAttentionSurprise,Seq2SeqAttentionFear
train_datas, val_datas, test_datas = split_data()
keys = ['posts', 'postLen', 'resps', 'respLen', 'resp_tfidf']
train_datas = [train_datas[k] for k in keys]
val_datas = [val_datas[k] for k in keys]
print("train num:%s" % len(train_datas[0]))
seq_len = 20
batch_size = 128
D_step = 5
G_step = 1
is_debug = True
# Emotion Classifier
emo_clas = Classification(sequence_length=20, num_classes=6, l2_reg_lambda=0.1)
emo_clas.restore_last_session(base_path="./emo_cls")
import data import utils import log log = log.log log.struct_log(log) pre_method = 'Rescaling' train_method = 'RandomForest' time_train = 300 time_test = 10 n_stock_select = 10 seed = 41 data = data.data data.read_data(data) data.split_data(data) data.pre_process(data,pre_method) model = Model.Model model.read_data(model) model.roll_train(model,train_method,time_train,time_test) utils = utils.utils utils.parameter(utils,n_stock_select,seed) utils.struct_strategy(utils) utils.merging_index(utils) log.logger.info(utils.strategy) utils.print_winrate(utils) utils.plot_value(utils)
model_files = glob.glob('models/*.hdf5')
other_models = glob.glob('models/*/*-0.6*hdf5')
model_files.extend(other_models)
public_test_dict = {}
private_test_dict = {}
results = {}
for model_file in model_files:
model = load_model(model_file, compile=False)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
input_shape = model.input_shape[1:4]
if input_shape not in public_test_dict.keys():
faces, emotions = load_emotion_data('data/fer2013/fer2013.csv', input_shape)
train, test = split_data(faces, emotions, 0.2)
public_test_dict[input_shape], private_test_dict[input_shape] = split_data(test[0], test[1], 0.5)
start = time.time()
public_test_result = model.evaluate(public_test_dict[input_shape][0], public_test_dict[input_shape][1])
private_test_result = model.evaluate(private_test_dict[input_shape][0], private_test_dict[input_shape][1])
duration = time.time() - start
print(model_file)
print('public test', public_test_result)
print('private test', private_test_result)
results[model_file] = {'public_acc': public_test_result[1], 'private_acc': private_test_result[1], 'time': duration}
print(results)
import json
json.dump(results, open('test.json', 'w'))
def main():
global ADV_WEIGHT, TRANSFER_WEIGHT
# set random seed
np.random.seed(42)
torch.manual_seed(42)
torch.backends.cudnn.deterministic = True
# Parsing arguments
parser = argparse.ArgumentParser(description='signer-independent project')
parser.add_argument('--model', type=str, required=True)
parser.add_argument('--dataset', type=str, required=True)
parser.add_argument('--mode', type=str, default='test')
parser.add_argument('--gpu', type=int, required=True)
parser.add_argument('--adv_weight', type=float, required=True)
parser.add_argument('--transf_weight', type=float, required=True)
parser.add_argument('--output', default='./output_cnn/')
args = parser.parse_args()
# set adversarial and transfer weights
TRANSFER_WEIGHT = args.transf_weight
ADV_WEIGHT = args.adv_weight
# Make output direcotiry if not exists
if not os.path.isdir(args.output):
os.mkdir(args.output)
# select gpu
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# dataset
dataset = DATASETS_LIST[args.dataset](model=args.model)
X_to_split = np.zeros((len(dataset), 1))
print(len(dataset[0]))
# evaluation protocol
(IM_SIZE, MODE, SPLITS, n_signers,
n_classes) = get_evaluation_protocol(args.dataset)
# get data splitter
dataSplitter = getSplitter(dataset,
n_splits=SPLITS,
mode=MODE,
test_size=.10)
results = []
split = 0
for split, (tr_indexes, test_indexes) in enumerate(dataSplitter):
output_fn = os.path.join(args.output, 'split_' + str(split))
if not os.path.isdir(output_fn):
os.mkdir(output_fn)
# split data
(train_loader, valid_loader,
test_loader) = split_data(dataset, (tr_indexes, test_indexes),
BATCH_SIZE,
dataAug=True,
mode=MODE)
# Initialize the model
model = MODEL_LIST[args.model](input_shape=IM_SIZE,
output_signers=n_signers,
output_classes=n_classes,
hasAdversial=True).to(device)
print(model)
# Train or test
if args.mode == 'train':
# Fit model
model, train_history, valid_loader = fit(model=model,
data=(train_loader,
valid_loader),
device=device,
output=output_fn,
n_signers=n_signers)
# save train history
res_fn = os.path.join(*(output_fn, '_history.pckl'))
pickle.dump(train_history, open(res_fn, "wb"))
elif args.mode == 'test':
model.load_state_dict(
torch.load(os.path.join(*(output_fn, 'cnn.pth'))))
# load train history
res_fn = os.path.join(*(output_fn, '_history.pckl'))
train_history = pickle.load(open(res_fn, "rb"))
plot_fn = os.path.join(*(output_fn, 'cnn_history.png'))
plot_train_history(train_history, plot_fn=plot_fn)
# Test results
(_, test_loss, _, _, _, test_acc, test_acc_3,
test_acc_5) = eval_model(model,
test_loader,
n_signers,
device,
debug=True)
print('##!!!! Test loss: {:.5f} |'.format(test_loss.item()) +
' Test Acc: {:.5f}'.format(test_acc))
results.append((test_loss.item(), test_acc, test_acc_3, test_acc_5))
# TSNE maps
# tsne(model, test_loader, device,
# plot_fn=os.path.join(*(output_fn, 'tsne.png')))
# save results
print(results)
# asdas
res_fn = os.path.join(args.output, 'res.pckl')
pickle.dump(results, open(res_fn, "wb"))
results = pickle.load(open(res_fn, "rb"))
# Compute average and std
print(results)
acc_array = np.array([i[1] for i in results])
acc3_array = np.array([i[2] for i in results])
acc5_array = np.array([i[3] for i in results])
print('Average acc: ', np.mean(acc_array))
print('Average acc3: ', np.mean(acc3_array))
print('Average acc5: ', np.mean(acc5_array))
print('Std acc: ', np.std(acc_array))
plt.ylabel(test_x.iloc[:, 1].name)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())
model_accuracy = trained_model.score(test_x, test_y)
print("train_accuracy::", model_accuracy)
return fig
if __name__ == '__main__':
doctors = read_diabetes()
train_x, test_x, train_y, test_y = split_data(doctors)
features = ['glucose', 'mass']
train_x, test_x = select_features(train_x, test_x, features)
# Training Logistic regression model
clf = train_logistic_regression(train_x, train_y)
#clf = eval(classifier+"()")
#clf = MLPClassifier()
#clf.fit(train_x, train_y)
fig = visualize_plot(clf, test_x, test_y)
plt.savefig('./static/visualize_plot.png')
plt.show()
def preproc_data():
from data import split_data
split_data('/content/itr/itr/data/hin-eng/hin.txt',
'/content/itr/itr/data/hin-eng')
import data import baseline import numpy as np x_data, y_data = data.get_keras_data() x_data = [" ".join(x.split()[:100]) for x in x_data] X_train, X_val, y_train, y_val = data.split_data(x_data,y_data) X_train, X_val = data.tf_idf(X_train,X_val) #get baseline accuracy models = baseline.Base_models(X_train, y_train, X_val, y_val) print(models.BinaryRe()) print(powerset()) print(mlknn())
Muraro = data.read_dataset(_path+"../data/Muraro/data.h5")
Enge = data.read_dataset(_path+"../data/Enge/data.h5")
Segerstolpe = data.read_dataset(_path+"../data/Segerstolpe/data.h5")
Xin_2016 = data.read_dataset(_path+"../data/Xin_2016/data.h5")
Lawlor = data.read_dataset(_path+"../data/Lawlor/data.h5")
merge = {'Baron_human':Baron_human, 'Muraro':Muraro, 'Enge':Enge, 'Segerstolpe':Segerstolpe,
'Xin_2016':Xin_2016, 'Lawlor':Lawlor}
mergedexpr, mergedl = data.merge_datasets(merge)
s = mergedexpr.sum(axis=1)
x = (mergedexpr.T/s).T
x = x*10000
#x = x[: ,:1000]
whole_set = dataset.Single(x, mergedl)
x,y,z,w = data.split_data(x, mergedl)
whole_set.print_info()
for
exit()
x = np.load("./data/train15720data.npy")
z = np.load("./data/train15720label.npy")
y = np.load("./data/test15720data.npy")
w = np.load("./data/test15720label.npy")
train_set = dataset.Single(x, z)
test_set = dataset.Single(y, w)
dl = DataLoader(train_set, batch_size=60, shuffle=True)
def main(disp_text=True):
if config.fresh_model:
config.all_losses = []
save_model(make_model())
model = load_model()
if disp_text: print('created model.', end=' ')
else:
model = load_model()
if not model:
save_model(make_model())
model = load_model()
if disp_text: print('created model.', end=' ')
else:
if disp_text: print('loaded model.', end=' ')
data = load_data()
data, data_dev = split_data(data)
# from random import choice
# from torch import randn
# data = [[randn(config.in_size) for _ in range(choice(range(config.max_seq_len//2,config.max_seq_len)))] for _ in range(40)]
# data_dev = []
# for d in data: print(len(d))
if config.max_seq_len: data = [d[:config.max_seq_len] for d in data]
if not config.batch_size or config.batch_size >= len(data):
config.batch_size = len(data)
one_batch = True
elif config.batch_size < 1:
config.batch_size = int(len(data) * config.batch_size)
one_batch = False
else:
one_batch = False
if disp_text:
print(
f'hm data: {len(data)}, hm dev: {len(data_dev)}, bs: {config.batch_size}, lr: {config.learning_rate}, \ntraining started @ {now()}'
)
data_losss, dev_losss = [], []
if not one_batch:
if not config.all_losses:
config.all_losses.append(dev_loss(model, data))
data_losss.append(config.all_losses[-1])
if config.dev_ratio:
dev_losss.append(dev_loss(model, data_dev))
if data_losss or dev_losss:
if disp_text:
print(
f'initial loss(es): {data_losss[-1] if data_losss else ""} {dev_losss[-1] if dev_losss else ""}'
)
for ep in range(config.hm_epochs):
loss = 0
for i, batch in enumerate(batchify_data(data)):
loss += respond_to(model, batch)
sgd(model) if config.optimizer == 'sgd' else adaptive_sgd(model)
loss /= len(data)
if not one_batch: loss = dev_loss(model, data)
data_losss.append(loss)
config.all_losses.append(loss)
if config.dev_ratio: dev_losss.append(dev_loss(model, data_dev))
if disp_text:
print(
f'epoch {ep}, loss {loss}, dev loss {dev_losss[-1] if config.dev_ratio else ""}, completed @ {now()}',
flush=True)
if config.ckp_per_ep and ((ep + 1) % config.ckp_per_ep == 0):
save_model(model, config.model_path + f'_ckp{ep}')
if one_batch: data_losss.append(dev_loss(model, data))
if disp_text:
print(
f'training ended @ {now()} \nfinal losses: {data_losss[-1]}, {dev_losss[-1] if config.dev_ratio else ""}',
flush=True)
show(plot(data_losss))
if config.dev_ratio:
show(plot(dev_losss))
if not config.fresh_model: show(plot(config.all_losses))
return model, [data_losss, dev_losss]
plt.show()
################################################################################
################################################################################
################################################################################
################################################################################
## MAIN ########################################################################
################################################################################
if __name__ == '__main__':
X,Y = load_data_from_csv('../data/binary.csv', -1, float)
X,Y = bootstrap_data(X, Y, 25)
X = X[:,2:]
Xtr,Xte,Ytr,Yte = split_data(X, Y, .8)
knn = KNNClassify(Xtr, Ytr)
print(cols((X,knn.predict(X))))
plot_classify_2D(knn, X, Y)
################################################################################
################################################################################
################################################################################
def main():
if config.attention_only:
from model2 import make_model_higher, respond_to
else: from model import make_model_higher, respond_to
if config.fresh_model:
save_model(make_model_higher())
model = load_model()
print('created model.',end=' ')
else:
model = load_model()
if not model:
save_model(make_model_higher())
model = load_model()
print('created model.',end=' ')
else:
print('loaded model.',end=' ')
print(f'info: {config.creation_info}')
data = load_data(frames=not config.attention_only)
data, data_dev = split_data(data)
if not config.batch_size or config.batch_size >= len(data):
config.batch_size = len(data)
one_batch = True
elif config.batch_size < 1:
config.batch_size = int(len(data)*config.batch_size)
one_batch = False
else: one_batch = False
print(f'hm data: {len(data)}, hm dev: {len(data_dev)}, bs: {config.batch_size}, lr: {config.learning_rate}, \ntraining started @ {now()}')
data_losss, dev_losss = [], []
if config.batch_size != len(data):
data_losss.append(dev_loss(model, data))
if config.dev_ratio:
dev_losss.append(dev_loss(model, data_dev))
if data_losss or dev_losss:
print(f'initial loss(es): {data_losss[-1] if data_losss else ""} {dev_losss[-1] if dev_losss else ""}')
for ep in range(config.hm_epochs):
loss = 0
for i, batch in enumerate(batchify_data(data, do_shuffle=not one_batch)):
# print(f'\tbatch {i}, started @ {now()}', flush=True)
batch_size = sum(len(sequence) for sequence in batch)
loss += respond_to(model, batch)
sgd(model, batch_size=batch_size) if config.optimizer == 'sgd' else \
adaptive_sgd(model, batch_size=batch_size)
# loss /= sum(len(sequence) for sequence in data)
if not one_batch: loss = dev_loss(model, data)
data_losss.append(loss)
if config.dev_ratio:
dev_losss.append(dev_loss(model, data_dev))
print(f'epoch {ep}, loss {loss}, dev loss {dev_losss[-1] if config.dev_ratio else ""}, completed @ {now()}', flush=True)
if config.ckp_per_ep and ((ep+1)%config.ckp_per_ep==0):
save_model(model,config.model_path+f'_ckp{ep}')
# data_losss.append(dev_loss(model, data))
# if config.dev_ratio:
# dev_losss.append(dev_loss(model, data_dev))
print(f'training ended @ {now()} \nfinal losses: {data_losss[-1]}, {dev_losss[-1] if config.dev_ratio else ""}', flush=True)
show(plot(data_losss))
if config.dev_ratio:
show(plot(dev_losss))
# if input(f'Save model as {config.model_path}? (y/n): ').lower() == 'y':
# save_model(load_model(), config.model_path + '_prev')
# save_model(model)
return model, [data_losss, dev_losss]
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('corpus', help = 'the name of the input corpus file')
parser.add_argument('--seeds', help = 'percentage of seeds', type = float, default = 0.01)
parser.add_argument('--epochs', help = 'number of epochs', type = int, default = 40)
parser.add_argument('--learning_rate', help = 'learning rate', type = float, default = 1.0)
parser.add_argument('--param_reg', help = 'the regularization factor of the parameters', type = float, default = 0.001)
parser.add_argument('--ent_reg', help = 'the factor of entropy regularization', type = float, default = 0.0)
args = parser.parse_args()
lasagne.random.set_rng(np.random)
np.random.seed(0)
features, labels, label_set = data.read_content_citeseer(args.corpus)
split = data.split_data(labels, args.seeds)
maxf = get_maxf(features)
trainx, trainy = constuct_dataset(features, labels, label_set, split[0], maxf)
testx, testy = constuct_dataset(features, labels, label_set, split[1], maxf)
allx, ally = constuct_dataset(features, labels, label_set, features.keys(), maxf)
input_var = sparse.csr_matrix(name = 'x', dtype = 'float32')
un_var = sparse.csr_matrix(name = 'ux', dtype = 'float32')
target_var = T.imatrix('targets')
ent_target = T.ivector('ent_targets')
network, l_entropy = build_model(input_var, maxf + 1, trainy.shape[1], args.ent_reg > 0, un_var)
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean() + regularize_layer_params(network, l2) * args.param_reg
def prepare_data(self):
from data import split_data
split_data('/content/itr/hin.txt', '/content/itr/')
def main(FLAGS):
# set seed
np.random.seed(FLAGS.seed)
tf.set_random_seed(FLAGS.seed)
with tf.device('/cpu:0'), tf.name_scope('input'):
# load data
data, meta = load_data(FLAGS.dataset_root,
FLAGS.dataset,
is_training=True)
train_data, val_data = split_data(data, FLAGS.validate_rate)
batch_size = FLAGS.n_class_per_iter * FLAGS.n_img_per_class
img_shape = train_data[0].shape[1:]
# build DataSampler
train_data_sampler = DataSampler(train_data, meta['n_class'],
FLAGS.n_class_per_iter,
FLAGS.n_img_per_class)
val_data_sampler = DataSampler(val_data, meta['n_class'],
FLAGS.n_class_per_iter,
FLAGS.n_img_per_class)
# build tf_dataset for training
train_dataset = (tf.data.Dataset.from_generator(
lambda: train_data_sampler, (tf.float32, tf.int32),
([batch_size, *img_shape
], [batch_size])).take(FLAGS.n_iter_per_epoch).flat_map(
lambda x, y: tf.data.Dataset.from_tensor_slices((x, y))).map(
preprocess_for_train, 8).batch(batch_size).prefetch(1))
# build tf_dataset for val
val_dataset = (tf.data.Dataset.from_generator(
lambda: val_data_sampler, (tf.float32, tf.int32),
([batch_size, *img_shape], [batch_size])).take(100).flat_map(
lambda x, y: tf.data.Dataset.from_tensor_slices((x, y))).map(
preprocess_for_eval, 8).batch(batch_size).prefetch(1))
# clean up
del data, train_data, val_data
# construct data iterator
data_iterator = tf.data.Iterator.from_structure(
train_dataset.output_types, train_dataset.output_shapes)
# construct iterator initializer for training and validation
train_data_init = data_iterator.make_initializer(train_dataset)
val_data_init = data_iterator.make_initializer(val_dataset)
# get data from data iterator
images, labels = data_iterator.get_next()
tf.summary.image('images', images)
# define useful scalars
learning_rate = tf.placeholder(tf.float32, shape=(), name='learning_rate')
tf.summary.scalar('lr', learning_rate)
is_training = tf.placeholder(tf.bool, [], name='is_training')
global_step = tf.train.create_global_step()
# define optimizer
optimizer = tf.train.AdamOptimizer(learning_rate)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate)
# build the net
model = importlib.import_module('models.{}'.format(FLAGS.model))
net = model.Net(n_feats=FLAGS.n_feats, weight_decay=FLAGS.weight_decay)
if net.data_format == 'channels_first' or net.data_format == 'NCHW':
images = tf.transpose(images, [0, 3, 1, 2])
# get features
features = net(images, is_training)
tf.summary.histogram('features', features)
# summary variable defined in net
for w in net.global_variables:
tf.summary.histogram(w.name, w)
with tf.name_scope('losses'):
# compute loss, if features is l2 normed, then 2 * cosine_distance will
# equal squared l2 distance.
distance = 2 * custom_ops.cosine_distance(features)
# hard mining
arch_idx, pos_idx, neg_idx = custom_ops.semi_hard_mining(
distance, FLAGS.n_class_per_iter, FLAGS.n_img_per_class,
FLAGS.threshold)
# triplet loss
N_pair_lefted = tf.shape(arch_idx)[0]
def true_fn():
pos_distance = tf.gather_nd(distance,
tf.stack([arch_idx, pos_idx], 1))
neg_distance = tf.gather_nd(distance,
tf.stack([arch_idx, neg_idx], 1))
return custom_ops.triplet_distance(pos_distance, neg_distance,
FLAGS.threshold)
loss = tf.cond(N_pair_lefted > 0, true_fn, lambda: 0.)
pair_rate = N_pair_lefted / (FLAGS.n_class_per_iter *
FLAGS.n_img_per_class**2)
# compute l2 regularization
l2_reg = tf.losses.get_regularization_loss()
with tf.name_scope('metrics') as scope:
mean_loss, mean_loss_update_op = tf.metrics.mean(loss,
name='mean_loss')
mean_pair_rate, mean_pair_rate_update_op = tf.metrics.mean(
pair_rate, name='mean_pair_rate')
reset_metrics = tf.variables_initializer(
tf.get_collection(tf.GraphKeys.LOCAL_VARIABLES, scope))
metrics_update_op = tf.group(mean_loss_update_op,
mean_pair_rate_update_op)
# collect metric summary alone, because it need to
# summary after metrics update
metric_summary = [
tf.summary.scalar('loss', mean_loss, collections=[]),
tf.summary.scalar('pair_rate', mean_pair_rate, collections=[])
]
# compute grad
grads_and_vars = optimizer.compute_gradients(loss + l2_reg)
# summary grads
for g, v in grads_and_vars:
tf.summary.histogram(v.name + '/grad', g)
# run train_op and update_op together
train_op = optimizer.apply_gradients(grads_and_vars,
global_step=global_step)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = tf.group(train_op, *update_ops)
# build summary
jpg_img_str = tf.placeholder(tf.string, shape=[], name='jpg_img_str')
emb_summary_str = tf.summary.image(
'emb',
tf.expand_dims(tf.image.decode_image(jpg_img_str, 3), 0),
collections=[])
train_summary_str = tf.summary.merge_all()
metric_summary_str = tf.summary.merge(metric_summary)
# init op
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# prepare for the logdir
if not tf.gfile.Exists(FLAGS.logdir):
tf.gfile.MakeDirs(FLAGS.logdir)
# saver
saver = tf.train.Saver(max_to_keep=FLAGS.n_epoch)
# summary writer
train_writer = tf.summary.FileWriter(os.path.join(FLAGS.logdir, 'train'),
tf.get_default_graph())
val_writer = tf.summary.FileWriter(os.path.join(FLAGS.logdir, 'val'),
tf.get_default_graph())
# session
config = tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False,
intra_op_parallelism_threads=8,
inter_op_parallelism_threads=0)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# do initialization
sess.run(init_op)
# restore
if FLAGS.restore:
saver.restore(sess, FLAGS.restore)
lr_boundaries = list(map(int, FLAGS.boundaries.split(',')))
lr_values = list(map(float, FLAGS.values.split(',')))
lr_manager = LRManager(lr_boundaries, lr_values)
time_meter = TimeMeter()
# start to train
for e in range(FLAGS.n_epoch):
print('-' * 40)
print('Epoch: {:d}'.format(e))
# training loop
try:
i = 0
sess.run([train_data_init, reset_metrics])
while True:
lr = lr_manager.get(e)
fetch = [train_summary_str] if i % FLAGS.log_every == 0 else []
time_meter.start()
result = sess.run([train_op, metrics_update_op] + fetch, {
learning_rate: lr,
is_training: True
})
time_meter.stop()
if i % FLAGS.log_every == 0:
# fetch summary str
t_summary = result[-1]
t_metric_summary = sess.run(metric_summary_str)
t_loss, t_pr = sess.run([mean_loss, mean_pair_rate])
sess.run(reset_metrics)
spd = batch_size / time_meter.get_and_reset()
print(
'Iter: {:d}, LR: {:g}, Loss: {:.4f}, PR: {:.2f}, Spd: {:.2f} i/s'
.format(i, lr, t_loss, t_pr, spd))
train_writer.add_summary(t_summary,
global_step=sess.run(global_step))
train_writer.add_summary(t_metric_summary,
global_step=sess.run(global_step))
i += 1
except tf.errors.OutOfRangeError:
pass
# save checkpoint
saver.save(sess,
'{}/{}'.format(FLAGS.logdir, FLAGS.model),
global_step=sess.run(global_step),
write_meta_graph=False)
# val loop
try:
sess.run([val_data_init, reset_metrics])
v_flist, v_llist = [], []
v_iter = 0
while True:
v_feats, v_labels, _ = sess.run(
[features, labels, metrics_update_op],
{is_training: False})
if v_iter < FLAGS.n_iter_for_emb:
v_flist.append(v_feats)
v_llist.append(v_labels)
v_iter += 1
except tf.errors.OutOfRangeError:
pass
v_loss, v_pr = sess.run([mean_loss, mean_pair_rate])
print('[VAL]Loss: {:.4f}, PR: {:.2f}'.format(v_loss, v_pr))
v_jpg_str = feat2emb(
np.concatenate(v_flist, axis=0), np.concatenate(v_llist, axis=0),
TSNE_transform if int(FLAGS.n_feats) > 2 else None)
val_writer.add_summary(sess.run(metric_summary_str),
global_step=sess.run(global_step))
val_writer.add_summary(sess.run(emb_summary_str,
{jpg_img_str: v_jpg_str}),
global_step=sess.run(global_step))
print('-' * 40)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--job-dir",
default="",
help="Job directory for output plots")
parser.add_argument("--data-path",
default="diabetes.csv",
help="Data directory for PIMA")
parser.add_argument("--n-folds",
type=int,
default=10,
help="Number of Folds for K-Fold Cross Validation")
parser.add_argument("--n-trees",
type=int,
default=100,
help="Number of Trees")
parser.add_argument("--n-neighbors",
type=int,
default=10,
help="Number of neighbors for critical set")
parser.add_argument("--max-depth",
type=int,
default=10,
help="Depth of search for random forest")
parser.add_argument("--min-size",
type=int,
default=1,
help="Minimum size for random forest")
parser.add_argument("--n-features",
type=int,
default=2,
help="Number of features for random forest")
parser.add_argument("--sample-size",
type=float,
default=1.0,
help="Sample size for random forest")
parser.add_argument("--p-critical",
type=float,
default=0.5,
help="Percentage of forest size using critical set")
parser.add_argument("--seed", type=int, default=0, help="Random seed")
args = parser.parse_args()
# Set seeds for reproducibility
np.random.seed(seed=args.seed)
seed(args.seed)
# Prep data for model training
data = load_data(args.data_path)
raw_data_train, raw_data_test = split_data(data)
data_train, scaler, medians = pima_training_data_transformation(
raw_data_train)
# Evaluate algorithm on training data using K-Fold cross validation
_ = evaluate_algorithm(data_train, biased_random_forest, args.n_folds,
args.n_neighbors, args.p_critical, args.max_depth,
args.min_size, args.sample_size, args.n_trees,
args.n_features)
# Train tree model on full training dataset
trees = train_biased_random_forest(data_train, args.n_neighbors,
args.max_depth, args.min_size,
args.sample_size, args.n_trees,
args.n_features, args.p_critical)
# Evaluate model on test data
# Prepare test data
data_test = pima_test_data_transformation(raw_data_test, scaler,
medians).to_numpy()
test_set = list()
for row in data_test:
row_copy = list(row)
test_set.append(row_copy)
row_copy[-1] = None
# Run inference on test set
test_predictions, test_probs = test_random_forest(trees, test_set)
test_actual = data_test[:, -1]
# Evaluate test data performance
print('Test Data Performance')
fp_rates, tp_rates, recalls, precisions = display_metrics(
test_actual, test_predictions, test_probs)
# Plot final
outname = "Test Data"
save_prc_curve(recalls, precisions, name=outname)
save_roc_curve(fp_rates, tp_rates, name=outname)
# LIME
df_features = data_train.iloc[:, :-1]
feature_cols = df_features.columns
data_features = df_features.values
data_labels = data_train.iloc[:, -1].values
explainer = lime.lime_tabular.LimeTabularExplainer(
data_features,
mode='classification',
training_labels=data_labels,
feature_names=feature_cols)
model = BiasedRandomForestModel(trees)
# ipdb is useful here for further exploration in LIME. This can also be moved to a follow-up notebook.
# ipdb.set_trace()
idx = 0
exp = explainer.explain_instance(data_features[idx],
model.get_probs,
num_features=7)
exp.save_to_file('lime_rf_example0.html')
def main():
load_dotenv('.env.general')
config = load_config('config.yml')
Path(config.logging.handlers.debug_file_handler.filename).parent.mkdir(
parents=True, exist_ok=True)
Path(config.logging.handlers.info_file_handler.filename).parent.mkdir(
parents=True, exist_ok=True)
logging.config.dictConfig(config.logging)
_logger.info("Loading the data")
x, y = load_training_data()
x_train, x_test, y_train, y_test = split_data(x, y)
with tempfile.TemporaryDirectory() as td:
temp_dir = Path(td)
mlflow.set_experiment(config.experiment.name)
params = {}
tags = {}
metrics = {}
artifacts = {}
with mlflow.start_run():
_logger.info("Fitting the preprocessor")
preprocessor = get_preprocessor()
preprocessor.fit(x_train, y_train)
_logger.info("Preprocessing the training data")
x_train_prep = preprocessor.transform(x_train)
x_test_prep = preprocessor.transform(x_test)
estimator_params, search_space = get_params()
if search_space is None:
estimator, estimator_tags, estimator_metrics, estimator_artifacts = train_run(
estimator_params=estimator_params,
x_train_prep=x_train_prep,
y_train=y_train,
x_test_prep=x_test_prep,
y_test=y_test,
temp_dir=temp_dir)
model = make_pipeline(preprocessor, estimator)
params.update(
{f"estimator_{k}": v
for k, v in estimator_params.items()})
tags.update(
{f"estimator_{k}": v
for k, v in estimator_tags.items()})
metrics.update(estimator_metrics)
artifacts.update(estimator_artifacts)
else:
def hyperopt_objective(search_params):
# This function is called for each set of hyper-parameters being tested by HyperOpt.
run_name = str(len(trials) - 1)
ho_params = {}
ho_tags = {}
ho_metrics = {}
ho_artifacts = {}
search_params = flatten_params(search_params)
search_params = prep_params(search_params)
ho_estimator_params = estimator_params.copy()
ho_estimator_params.update(search_params)
with mlflow.start_run(nested=True, run_name=run_name):
ho_estimator, ho_estimator_tags, ho_estimator_metrics, ho_estimator_artifacts = train_run(
estimator_params=ho_estimator_params,
x_train_prep=x_train_prep,
y_train=y_train,
x_test_prep=x_test_prep,
y_test=y_test,
temp_dir=temp_dir / run_name)
ho_model = make_pipeline(preprocessor, ho_estimator)
ho_params.update({
f"estimator_{k}": v
for k, v in ho_estimator_params.items()
})
ho_tags.update({
f"estimator_{k}": v
for k, v in ho_estimator_tags.items()
})
ho_metrics.update(ho_estimator_metrics)
ho_artifacts.update(ho_estimator_artifacts)
ho_tags['hyperopt'] = True
log_sk_model(ho_model,
registered_model_name=None,
params=ho_params,
tags=ho_tags,
metrics=ho_metrics,
artifacts=ho_artifacts)
loss = 1 - ho_metrics[config.evaluation.primary_metric]
return {
'loss': loss,
'status': STATUS_OK,
'model': ho_model,
'params': ho_params,
'tags': ho_tags,
'metrics': ho_metrics,
'artifacts': ho_artifacts
}
trials = Trials()
fmin(fn=hyperopt_objective,
space=search_space,
algo=tpe.suggest,
trials=trials,
max_evals=config.training.max_evals,
rstate=np.random.RandomState(1),
show_progressbar=False)
model = trials.best_trial['result']['model']
params = trials.best_trial['result']['params']
tags = trials.best_trial['result']['tags']
metrics = trials.best_trial['result']['metrics']
artifacts = trials.best_trial['result']['artifacts']
if config.evaluation.shap_analysis:
_logger.info("Starting shap analysis")
shap_tags, shap_artifacts = shap_analyse(
model=model, x=x_train, temp_dir=Path(temp_dir) / 'shap')
tags.update(shap_tags)
artifacts.update(shap_artifacts)
else:
_logger.info("Shap analysis skipped")
log_sk_model(model,
registered_model_name=None,
params=params,
tags=tags,
metrics=metrics,
artifacts=artifacts)
return (x_train, y_train, x_test,
y_test), model, params, tags, metrics, artifacts
args = parser.parse_args()
# for TPU
os.environ["WANDB_API_KEY"] = "0" # to silence warning
device = xm.xla_device()
print('Found TPU at: {}'.format(device))
# For reproducibility
np.random.seed(args.seed)
# Open train and test csv files using pandas library
train_df = pd.read_csv(args.train_file)
test_df = pd.read_csv(args.test_file)
# Split training dataset into two parts - the data we will train the model with and a validation set.
train_df, validation_df = data.split_data(train_df)
# Check the number of rows and columns in the subsets after split
print("Train data shape after split: {} \n".format(train_df.shape))
print("Validation data shape after split: {} \n".format(
validation_df.shape))
# Augment training data
train_df = data.augment_data(train_df,
test_df,
use_xnli=args.load_xnli,
use_mnli=args.load_mnli,
use_bt=args.back_translate,
bt_filepath=args.bt_file)
# Define the tokenizer to preprocess the input data
