def on_dataset_change(args):
with dispatcher.get_lock('bootenvs'):
if args['operation'] == 'create':
bootenvs.propagate(args, convert_bootenv)
if args['operation'] == 'delete':
logger.warn(args)
for i in args['ids']:
pool, dataset = split_dataset(i)
if pool != boot_pool_name:
continue
realname = dataset.split('/')[-1]
ds = bootenvs.query(('realname', '=', realname), single=True)
if ds:
bootenvs.remove(ds['id'])
if args['operation'] == 'update':
for i in args['entities']:
pool, dataset = split_dataset(i['id'])
if pool != boot_pool_name:
continue
realname = dataset.split('/')[-1]
ds = bootenvs.query(('realname', '=', realname), single=True)
if not ds:
continue
nickname = i.get('properties.beadm:nickname.value', realname)
if nickname and nickname != ds['id']:
bootenvs.rename(ds['id'], nickname)
bootenvs.put(nickname, convert_bootenv(i))
def make_dataset(name):
inputs, psi, reference = load('{}.data'.format(name))
# train/test split
trainset, testset = utils.split_dataset( # train/test split
utils.shuffle((inputs, psi)), # shuffle dataset
ratio=0.7)
# test/valid split
testset, validset = utils.split_dataset(testset, ratio=0.5)
return (trainset, testset, validset)
def transfer(source_model, target_path, frozen_layer):
# load data of branch
df = pd.read_pickle(target_path)
# create dataframe with netto sales, month, weekday, year
df = pd.DataFrame(data=df.values, index=df.index, columns=['netto'])
df = df.assign(month=df.index.month)
df = df.assign(weekday=df.index.weekday)
df = df.assign(year=df.index.year)
# split into train and test
train, test = split_dataset(df.values, 365)
# prepare input data for branch
n_input = 365
train_x, train_y = to_supervised(train, n_input, 365)
# load pre-trained model of source branch as base model
base_model = load_model(source_model)
# freeze specific layers of base model
for layer in base_model.layers[:frozen_layer]:
layer.trainable = False
print("frozen layers: " + str(frozen_layer))
# compile the model
base_model.compile(loss='mse', optimizer='adam')
# fit base_model with new data from branch
n_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]
input_data = [train_x[:, :, i].reshape((train_x.shape[0], n_timesteps, 1)) for i in range(n_features)]
base_model.fit(input_data, train_y, epochs=20, batch_size=16, verbose=0)
# evaluate fitted model
mape = evaluate_model(train, test, base_model)
return mape
def get_feats_from_csv_in_partitions():
"""
Extract the original features that are distributed in the dataset. Features
are splitted according with the config.yaml file.
"""
conf = utils.get_config()
rows = [
row for row in utils.load_csv()
if utils.check_filter(row, conf['filters'])
]
train_rows, valid_rows, test_rows = utils.split_dataset(
rows,
conf['valid_percent'],
conf['test_percent'],
rng=conf['rng_seed'])
X_train, y_train, X_valid, y_valid, X_test, y_test = [], [], [], [], [], []
prefixes = ['t_', 'i_', 's_'] # Feature names' prefixes
datasets = [(X_train, y_train, train_rows), (X_test, y_test, test_rows),
(X_valid, y_valid, valid_rows)]
out = []
for X, y, rows in datasets:
for row in rows:
X.append([
float(v) for k, v in row.iteritems()
if len(filter(k.startswith, prefixes)) > 0
])
y.append(int(row['classification'] == 'Malign'))
out.extend((np.asarray(X), np.asarray(y)))
return out
def get_feats_from_imagenet_in_partitions():
conf = utils.get_config()
imagenet_data = os.path.join(conf['models_path'], 'decafnet',
'imagenet.decafnet.epoch90')
imagenet_meta = os.path.join(conf['models_path'], 'decafnet',
'imagenet.decafnet.meta')
net = DecafNet(imagenet_data, imagenet_meta)
rows = utils.get_filtered_rows()
sets = utils.split_dataset(rows,
conf['valid_percent'],
conf['test_percent'],
rng=conf['rng_seed'])
feats = []
ys = []
for s in sets:
X = np.zeros((len(s), 4096))
y = np.zeros(len(s))
for i, row in enumerate(s):
try:
log.info('processing %i-th of %i' % (i, len(s)))
origin, im = utils.extract_roi(row, 30, True)
scores = net.classify(np.asarray(im), center_only=True)
X[i] = net.feature('fc7_cudanet_out')
y[i] = utils.is_positive(row)
except:
continue
feats.append(X)
ys.append(y)
return feats[0], ys[0], feats[1], ys[1], feats[2], ys[2]
def get_feats_in_partitions():
"""
Extracts features from all dataset and split them in train validation and
test sets
"""
conf = utils.get_config()
paths = utils.get_paths()
rows = utils.load_csv()
filters = conf['filters']
region_size = conf['region_size']
region_stride = conf['region_stride']
filtered_rows = [
row for row in rows if utils.check_filter(row, conf['filters'])
]
train_rows, valid_rows, test_rows = utils.split_dataset(
filtered_rows,
conf['valid_percent'],
conf['test_percent'],
rng=conf['rng_seed'])
conv = get_fprop_fn(False)
print 'Getting features from train...'
X_train = get_feats_from_rows(train_rows, conv, conf['stride'])
print 'Getting features from valid...'
X_valid = get_feats_from_rows(valid_rows, conv, conf['stride'])
print 'Getting features from test...'
X_test = get_feats_from_rows(test_rows, conv, conf['stride'])
y_train = [row['classification'] == 'Malign' for row in train_rows]
y_valid = [row['classification'] == 'Malign' for row in valid_rows]
y_test = [row['classification'] == 'Malign' for row in test_rows]
return X_train, y_train, X_valid, y_valid, X_test, y_test
def initialize_train_test(self, train_ratio=0.75, sample=True, full=False):
'''
This function initializes the training and the test sets.
The training set is initialized to a fraction of the original dataset given by
the input parameter train_ratio, while the test set will be initialized to the
remaining part of the original dataset. Notice that the training and the test
sets are copies of the original dataset, so every changes made on those datasets
will not affect the original one. The default value for the parameter train_ratio
is 0.75.
If the input parameter sample is True, then the training set is built sampling
rows from the original dataset. Otherwise, it is built taking the first part of
the dataset. Default value for sample is True.
If the input parameter full is True, then the values of the other parameters are
ignored and both the training and the test sets are initialized to be exact
copies of the full original one.
'''
if full:
self.train = self.dataset.copy()
self.test = self.dataset.copy()
else:
if sample:
self.train, self.test = utils.split_dataset_sample(
self.dataset, train_ratio)
else:
self.train, self.test = utils.split_dataset(
self.dataset, train_ratio)
def get_feats_from_imagenet_in_partitions():
conf = utils.get_config()
imagenet_data = os.path.join(
conf['models_path'], 'decafnet', 'imagenet.decafnet.epoch90')
imagenet_meta = os.path.join(
conf['models_path'], 'decafnet', 'imagenet.decafnet.meta')
net = DecafNet(imagenet_data, imagenet_meta)
rows = utils.get_filtered_rows()
sets = utils.split_dataset(
rows, conf['valid_percent'], conf['test_percent'], rng=conf['rng_seed'])
feats = []
ys = []
for s in sets:
X = np.zeros((len(s), 4096))
y = np.zeros(len(s))
for i, row in enumerate(s):
try:
log.info('processing %i-th of %i' % (i, len(s)))
origin, im = utils.extract_roi(row, 30, True)
scores = net.classify(np.asarray(im), center_only=True)
X[i] = net.feature('fc7_cudanet_out')
y[i] = utils.is_positive(row)
except:
continue
feats.append(X)
ys.append(y)
return feats[0], ys[0], feats[1], ys[1], feats[2], ys[2]
def get_feats_in_partitions():
"""
Extracts features from all dataset and split them in train validation and
test sets
"""
conf = utils.get_config()
paths = utils.get_paths()
rows = utils.load_csv()
filters = conf['filters']
region_size = conf['region_size']
region_stride = conf['region_stride']
filtered_rows = [
row for row in rows if utils.check_filter(row, conf['filters'])]
train_rows, valid_rows, test_rows = utils.split_dataset(
filtered_rows, conf['valid_percent'], conf['test_percent'], rng=conf['rng_seed'])
conv = get_fprop_fn(False)
print 'Getting features from train...'
X_train = get_feats_from_rows(
train_rows, conv, conf['stride'])
print 'Getting features from valid...'
X_valid = get_feats_from_rows(
valid_rows, conv, conf['stride'])
print 'Getting features from test...'
X_test = get_feats_from_rows(
test_rows, conv, conf['stride'])
y_train = [row['classification'] == 'Malign' for row in train_rows]
y_valid = [row['classification'] == 'Malign' for row in valid_rows]
y_test = [row['classification'] == 'Malign' for row in test_rows]
return X_train, y_train, X_valid, y_valid, X_test, y_test
def xgb_boost_model():
df_all = pickle.load(open("../output/features/basic_features.pkl", 'r'))
test_ind = df_all.relevance == -1
test_data = df_all[test_ind]
train_data = df_all[~test_ind]
test_data = test_data.drop(['relevance'], axis=1)
le = preprocessing.LabelEncoder()
le.fit(train_data['relevance'])
ids = test_data['id']
train, test, hold_out = utils.split_dataset(train_data)
relevant_columns =['title_similarity', 'product_desc_similarity', 'title_similarity_common', 'product_desc_similarity_common', 'description_length', 'search_length']
dTrain = xgb.DMatrix(train['X'][relevant_columns], label=train['Y'])
dTest = xgb.DMatrix(test['X'][relevant_columns], label=test['Y'])
dHold_out = xgb.DMatrix(hold_out['X'][relevant_columns], label=hold_out['Y'])
dSubmit = xgb.DMatrix(test_data[relevant_columns])
param = {'bst:max_depth':5 , 'bst:eta':0.05, 'silent':1, 'objective':'reg:linear', 'eval_metric':'rmse'}
evallist = [(dTest, 'eval'), (dTrain, 'train')]
numRound = 200
bst = xgb.train(param, dTrain, numRound, evallist)
predHoldout = bst.predict(dHold_out)
print "Mean square hold out error ", utils.rmse(hold_out['Y'], predHoldout)
predY = bst.predict(dSubmit)
utils.debug_model(hold_out['X'], hold_out['Y'], predY)
def run_network(window, model=None, save_model=False, show_plot=False):
start_time = time.time()
print('loading and prepare data set...')
data = read_dataset('../datasets/internet-traffic-data-5minutes.csv')
X_train, y_train, X_test, y_test, mean, std = split_dataset(
data, window, ratio=0.90, standardize=True)
print('number of training samples ', len(y_train))
print('number of test samples ', len(y_test))
if not model:
print('initialize model...')
model = compile_model(
hidden_neurons=25, loss_fn='mse',
input_dim=sum(1 for x in window if x), activation_fn='tanh')
print('model ', model.summary())
print('train model...')
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
model.fit(X_train, y_train, nb_epoch=500, validation_split=0.1,
callbacks=[early_stopping])
print('make predictions...')
prediction = model.predict(X_test).flatten()
if show_plot:
plot_result(prediction, y_test, mean, std)
print('mase = ', mase(y_train, y_test, prediction))
if save_model:
store_model(model)
print('totoal duration: {:.2f} seconds'.format(time.time() - start_time))
def main():
model = create_model()
model.summary()
# Building Phase
data = import_data("./dataset/crx_clean.data.txt")
X, Y, X_train, X_test, Y_train, Y_test = split_dataset(data)
# Expand data dimension for kernel to convolve over
X_train = np.expand_dims(X_train, axis=2) # (None, 46, 1)
X_test = np.expand_dims(X_test, axis=2) # (None, 46, 1)
# create model
model = KerasClassifier(build_fn=create_model, verbose=0)
# Operational Phase
scorer = make_scorer(f1_score, pos_label='+')
print("\n### GRID SEARCH CROSS VAL USING STRATIFIED K FOLD###\n")
Y_pred_grid_search = grid_search_cv_CNN(model, X_train, Y_train, X_test,
Y_test, scorer)
Y_pred_grid_search = np.squeeze(Y_pred_grid_search)
print()
print()
print(Y_pred_grid_search)
print()
print(Y_test)
print()
print_scores(Y_test, Y_pred_grid_search)
def fit(self, X, y):
"""fit tree in X, y"""
try:
# only for numpy arrays for now
if not isinstance(X, np.ndarray):
X = np.array(X)
if not isinstance(y, np.ndarray):
y = np.array(y)
if self.max_depth is None:
# The absolute maximum depth would be N−1, where N is the number of training samples.
# https://stats.stackexchange.com/questions/65893/maximal-depth-of-a-decision-tree
self.max_depth = X.shape[0] - 1
if self.n_clasess is None:
self.n_clasess = len(set(y))
assert (X.shape[0] > self.min_samples_split)
if not (self.max_depth is None):
assert (self.max_depth > 0)
gain, column_idx, threshold = self._find_best_split(X, y)
X_left, X_right, y_left, y_right = split_dataset(X,
y,
column=column_idx,
t=threshold)
self.node = Node(feature_idx=column_idx,
threshold=threshold,
labels=y,
gain=gain)
# build left and right child for max
self.node.left = DecisionTree(criterion=self.criterion,
debug=self.debug,
max_depth=self.max_depth - 1)
# if base class for random forest -> remove that attribute
self.node.left.x_columns = self.x_columns
self.node.left.n_clasess = self.n_clasess
self.node.left.fit(X_left, y_left)
self.node.right = DecisionTree(criterion=self.criterion,
debug=self.debug,
max_depth=self.max_depth - 1)
# if base class for random forest -> remove that attribute
self.node.right.x_columns = self.x_columns
self.node.right.n_clasess = self.n_clasess
self.node.right.fit(X_right, y_right)
# not the best idea, it is impossible to check for other conditions with assert :(
except AssertionError:
self.node = Node()
self._predict_from_leaf(y)
# test info about predictions
if self.debug:
print("Is Last Node: ", self.node.is_last)
print("Data shapes: ", X.shape, y.shape)
print("Y: ", y)
print("Prediction: ", self.node.node_prediction)
print("Predict proba: ", self.node.node_prob_prediction)
return self
def on_dataset_change(args):
if args['operation'] == 'create':
with dispatcher.get_lock('bootenvs'):
boot_pool = dispatcher.call_sync('zfs.pool.get_boot_pool')
bootenvs.propagate(args,
lambda x: convert_bootenv(boot_pool, x))
if args['operation'] == 'delete':
for i in args['ids']:
pool, dataset = split_dataset(i)
if pool != boot_pool_name:
continue
with dispatcher.get_lock('bootenvs'):
realname = dataset.split('/')[-1]
ds = bootenvs.query(('realname', '=', realname),
single=True)
if ds:
bootenvs.remove(ds['id'])
if args['operation'] == 'update':
boot_pool = None
for i in args['entities']:
pool, dataset = split_dataset(i['id'])
if pool != boot_pool_name:
continue
with dispatcher.get_lock('bootenvs'):
realname = dataset.split('/')[-1]
ds = bootenvs.query(('realname', '=', realname),
single=True)
if not ds:
continue
nickname = q.get(i, 'properties.beadm:nickname.value',
realname)
if nickname and nickname != ds['id']:
bootenvs.rename(ds['id'], nickname)
if not boot_pool:
boot_pool = dispatcher.call_sync(
'zfs.pool.get_boot_pool')
bootenvs.put(nickname, convert_bootenv(boot_pool, i))
def test_split_dataset(self):
filename = '/path/to/dataset.tsv'
train_filename = '/path/to/dataset_train.tsv'
dev_filename = '/path/to/dataset_dev.tsv'
read_data = 'Rock n Roll is a risk. You rick being ridiculed.\tDo you like rock music?\n' \
'Rock n Roll is a risk.\tDo you like rock?'
open_ = patch('utils.open', mock_open(read_data=read_data)).start()
open_.return_value.__iter__.return_value = read_data.split('\n')
writer = patch('csv.writer').start()
split_dataset(filename)
open_.assert_has_calls([
call(filename),
call(train_filename, 'w'),
call(dev_filename, 'w')
],
any_order=True)
writer.assert_called_with(open_.return_value, delimiter='\t')
writer.return_value.writerow.assert_called()
patch.stopall()
def on_dataset_change(args):
if args['operation'] == 'create':
with dispatcher.get_lock('bootenvs'):
boot_pool = dispatcher.call_sync('zfs.pool.get_boot_pool')
bootenvs.propagate(args, lambda x: convert_bootenv(boot_pool, x))
if args['operation'] == 'delete':
for i in args['ids']:
pool, dataset = split_dataset(i)
if pool != boot_pool_name:
continue
with dispatcher.get_lock('bootenvs'):
realname = dataset.split('/')[-1]
ds = bootenvs.query(('realname', '=', realname), single=True)
if ds:
bootenvs.remove(ds['id'])
if args['operation'] == 'update':
boot_pool = None
for i in args['entities']:
pool, dataset = split_dataset(i['id'])
if pool != boot_pool_name:
continue
with dispatcher.get_lock('bootenvs'):
realname = dataset.split('/')[-1]
ds = bootenvs.query(('realname', '=', realname), single=True)
if not ds:
continue
nickname = q.get(i, 'properties.beadm:nickname.value', realname)
if nickname and nickname != ds['id']:
bootenvs.rename(ds['id'], nickname)
if not boot_pool:
boot_pool = dispatcher.call_sync('zfs.pool.get_boot_pool')
bootenvs.put(nickname, convert_bootenv(boot_pool, i))
def do_experiment_for_one_year(run_path, year, config):
"""Performs the specified experiments for one year."""
X, Y = load_dataset(year, shuffle=config['experiment']['shuffle_data'])
if config['experiment']['type'] == 'single':
X_train, Y_train, X_test, Y_test = split_dataset(X, Y, config['experiment']['test_share'])
results = perform_one_experiment(X_train, Y_train, X_test, Y_test, config)
elif config['experiment']['type'] == 'cv':
results = perform_cv_runs(X, Y, config)
results_path = os.path.join(run_path, 'results_year{}.pkl'.format(year))
with open(results_path, 'wb') as f:
pickle.dump(results, f)
show_results(results, year, **config['analysis'])
def fit(self, samples, labels):
"""Train the model with the samples and lables provided according to
the parameters of the model."""
# Split into train and dev
x_train, y_train, x_dev, y_dev = split_dataset(samples, labels,
self.dev_share)
# Create batch iterator
if self.batch_iterator_type == 'normal':
batch_iter = _batch_iter
elif self.batch_iterator_type == 'oversample':
batch_iter = _oversampling_batch_iter
else:
raise ValueError('{} is not a valid batch_iterator_type'.format(
self.batch_iterator_type))
# Train model
train_batch_nr = []
train_loss_val = []
dev_batch_nr = []
dev_loss_val = []
for i, (x, y) in enumerate(
batch_iter(x_train, y_train, self.num_epochs,
self.batch_size)):
# Train
feed_dict = {
self.graph_nodes['x_input']: x,
self.graph_nodes['y_input']: y,
self.graph_nodes['dropout_keep_prob']: self.dropout_keep_prob
}
_, loss_val = self.sess.run(
[self.graph_nodes['optimize'], self.graph_nodes['loss']],
feed_dict=feed_dict)
train_batch_nr.append(i)
train_loss_val.append(loss_val)
if i % self.evaluate_every_n_steps == 0:
feed_dict = {
self.graph_nodes['x_input']: x_dev,
self.graph_nodes['y_input']: y_dev,
self.graph_nodes['dropout_keep_prob']: 1.
}
loss_val = self.sess.run(self.graph_nodes['loss'],
feed_dict=feed_dict)
dev_batch_nr.append(i)
dev_loss_val.append(loss_val)
if self.plot_training:
plt.plot(train_batch_nr, train_loss_val)
plt.plot(dev_batch_nr, dev_loss_val)
plt.show()
def pretrain_model(path):
df = pd.read_pickle(path)
# create dataframe with netto sales, month, weekday, year
df = pd.DataFrame(data=df.values, index=df.index, columns=['netto'])
df = df.assign(month=df.index.month)
df = df.assign(weekday=df.index.weekday)
df = df.assign(year=df.index.year)
# split into train and test
train, test = split_dataset(df.values, 365)
# evaluate model and get scores
model = evaluate_model(train, test)
# save model
model.save("models/" + path + ".h5")
del model
def run(self, id, updated_fields):
share = self.datastore.get_by_id('shares', id)
remove_unchanged(updated_fields, share)
path = self.dispatcher.call_sync('share.get_directory_path', share['id'])
try:
delete_config(
path,
'{0}-{1}'.format(share['type'], share['name'])
)
except OSError:
pass
if 'type' in updated_fields:
old_share_type = share['type']
new_share_type = self.dispatcher.call_sync('share.supported_types').get(updated_fields['type'])
if share['target_type'] == 'DATASET':
pool, dataset = split_dataset(share['target_path'])
self.join_subtasks(
self.run_subtask('volume.dataset.update', dataset, {
'permissions_type': new_share_type['perm_type']
})
)
share.update(updated_fields)
self.join_subtasks(self.run_subtask('share.{0}.delete'.format(old_share_type), id))
self.join_subtasks(self.run_subtask('share.{0}.create'.format(updated_fields['type']), share))
else:
self.join_subtasks(self.run_subtask('share.{0}.update'.format(share['type']), id, updated_fields))
if 'permissions' in updated_fields:
path = self.dispatcher.call_sync('share.translate_path', id)
self.join_subtasks(self.run_subtask('file.set_permissions', path, updated_fields['permissions']))
self.dispatcher.dispatch_event('share.changed', {
'operation': 'update',
'ids': [share['id']]
})
updated_share = self.datastore.get_by_id('shares', id)
path = self.dispatcher.call_sync('share.get_directory_path', updated_share['id'])
try:
save_config(
path,
'{0}-{1}'.format(updated_share['type'], updated_share['name']),
updated_share
)
except OSError as err:
self.add_warning(TaskWarning(errno.ENXIO, 'Cannot save backup config file: {0}'.format(str(err))))
def train_and_test(df, preds, seed):
'''
Run a single trial:
Shuffle df and split it into training and testing subsets
Train a new model based on the training sets
Test the model with testing set
Add prediction data into preds array
:param df: dataframe with full set of all available samples
columns: id, cat1 (primary class), cat2 (secondary),
title, titlen (claened title)
:param preds: an array of predictions, each prediction is a dictionary
cat: true category, pred: predicted category,
conf: model confidence in its prediction (< 1.0),
title: actual title of the chapter/sample
:return: average testing accuracy
'''
ret = {}
# PREPS
# randomly split the dataset
df = utils.split_dataset(
df,
settings.CAT_DEPTH,
settings.TRAIN_PER_CLASS_MIN,
settings.TEST_PER_CLASS,
settings.VALID_PER_CLASS,
)
# TRAIN
classifier = Classifier.from_name(settings.CLASSIFIER, seed)
classifier.set_datasets(df, titles_out_path)
classifier.train()
df_test = classifier.df_test
if settings.EVALUATE_TRAINING_SET:
evaluate_model(classifier,
classifier.df_train,
display_prefix='TRAIN = ')
accuracy = evaluate_model(classifier,
df_test,
preds,
display_prefix='TEST = ')
classifier_key = utils.get_exp_key(classifier)
classifier.release_resources()
return classifier_key, accuracy, classifier.df_train
def train(self, X_train, y_train):
"""
Train a Regression Forest using the given training data by training a number of Regression Trees
each with a random sample of the training data.
"""
train_dataset = np.c_[(X_train, y_train)]
for i in range(self.n_estimators):
bootstrap_sample = train_dataset[np.random.choice(
train_dataset.shape[0],
size=int(round(train_dataset.shape[0] * self.split)),
replace=True)]
X_train, y_train, _, _ = split_dataset(bootstrap_sample,
self.split,
is_print=False)
tree = RegressionTree(self.n_features, self.max_depth)
tree.train(X_train, y_train)
self.random_forest.append(tree)
def __init__(self, hparams=None):
super().__init__()
# Metrics
self.train_acc = pl.metrics.Accuracy()
self.val_acc = pl.metrics.Accuracy(compute_on_step=False)
self.test_acc = pl.metrics.Accuracy(compute_on_step=False)
# Hyperparameters
self.hparams = hparams
# Data
self.train_data, self.test_data, self.val_data = split_dataset()
# Model initialization
multiplier = 2 if hparams["bidirectional"] else 1
self.word_vec_size = 300 * multiplier
self.amount_classes = 7
self.rnn = nn.LSTM(input_size=self.word_vec_size,
hidden_size=hparams["lstm_hidden_dim"],
bidirectional=hparams["bidirectional"],
num_layers=hparams["lstm_num_layers"])
# First FC layer
modules = [
nn.Linear(self.word_vec_size, hparams["FC_layer_dims"][0]),
nn.ReLU(),
nn.Dropout(hparams["FC_dropouts"][0])
]
# Middle FC layers
for i, (dim, d_rate) in enumerate(
zip(hparams["FC_layer_dims"], hparams["FC_dropouts"])):
if i == len(hparams["FC_layer_dims"]) - 1:
continue # we reached the end
modules.append(nn.Linear(dim, hparams["FC_layer_dims"][i + 1]))
modules.append(nn.ReLU())
modules.append(nn.Dropout(d_rate))
# Last FC layer
modules.append(
nn.Linear(hparams["FC_layer_dims"][-1], self.amount_classes))
modules.append(nn.ReLU())
modules.append(nn.Dropout(hparams["FC_dropouts"][-1]))
self.classifier = nn.Sequential(*modules)
def main():
# Building Phase
data = import_data("./dataset/crx_clean.data.txt")
X, Y, X_train, X_test, Y_train, Y_test = split_dataset(data)
# Operational Phase
scorer = make_scorer(f1_score, pos_label='+')
print("\n### GRID SEARCH CROSS VAL USING STRATIFIED K FOLD###\n")
Y_pred_grid_search = grid_search_cv_mlp(X_train, Y_train, X_test, Y_test,
scorer)
print()
print()
print(Y_pred_grid_search)
print()
print(Y_test)
print()
print_scores(Y_test, Y_pred_grid_search)
def main(args: Namespace):
results_path = args.log_dir / str(datetime.now())
results_path.mkdir(exist_ok=True, parents=True)
write_args(results_path, vars(args))
fix_seed(args.seed)
height_model = 1000
width_model = 24
filenames_train, filenames_valid, filenames_test = split_dataset(args.data_root, args.fracs_dataset)
train_set = SeisDataset(filenames_train,
height_model=height_model,
width_model=width_model,
prob_aug=args.prob_aug)
valid_set = SeisDataset(filenames_valid,
height_model=height_model,
width_model=width_model,
prob_aug=args.prob_aug)
test_set = SeisDataset(filenames_test,
height_model=height_model,
width_model=width_model,
prob_aug=args.prob_aug)
net = UNetFB()
picker = Picker(net)
stopper = Stopper(args.n_wrongs, args.delta_wrongs)
trainer = Trainer(picker=picker, results_path=results_path,
train_set=train_set, valid_set=valid_set,
test_set=test_set, device=args.device,
batch_size=args.batch_size, lr=args.lr,
freq_valid=args.freq_valid, num_workers=args.num_workers,
dt_ms=args.dt_ms, height_model=height_model,
width_model=width_model, visual=args.visual,
stopper=stopper, weights=torch.tensor(args.weights))
trainer.train(num_epoch=args.num_epoch)
def run_custom_classifier(weights=None):
training_images = utils.load_images_from_dir(TRAINING_DIR)
testing_images = utils.load_images_from_dir(TESTING_DIR)
X_train, X_test, y_train, y_test = utils.split_dataset(training_images, testing_images, TRAINING_FILE, TESTING_FILE)
#preprocess data
X_train, y_train = preprocess(X_train, y_train)
X_test, y_test = preprocess(X_test, y_test)
#compile model
model = custom_classifier()
#train if no weights are passed in
if weights == None:
history = model.fit(X_train, y_train, epochs=50, verbose=1, validation_data=(X_test, y_test))
model.save_weights(os.path.join(WEIGHTS_DIR, 'custom_model.h5'))
else:
model.load_weights(weights)
scores = model.evaluate(X_train, y_train, verbose=1)
print('Digit 1 loss:', scores[1])
print('Digit 2 loss:', scores[2])
print('Digit 3 loss:', scores[3])
print('Digit 4 loss:', scores[4])
print('Digit 5 loss:', scores[5])
average_loss = sum([scores[i] for i in range(1, 6)]) / 5
print('Average loss:', average_loss)
print('Digit 1 accuracy:', scores[6])
print('Digit 2 accuracy:', scores[7])
print('Digit 3 accuracy:', scores[8])
print('Digit 4 accuracy:', scores[9])
print('Digit 5 accuracy:', scores[10])
average_accuracy = sum([scores[i] for i in range(6, 11)]) / 5
print('Average accueracy:', average_accuracy)
def obtain_train_test(path, ifilename, ftrain_name, ftest_name, frac_test=0.2):
df = pd.read_csv(os.path.join(path, ifilename))
df['date'] = pd.to_datetime(df['date'])
if 'Unnamed: 0' in df.columns:
df.drop('Unnamed: 0', axis=1, inplace=True)
groups = list(df['inlet'].unique())
dftrain, dftest = split_dataset(df, frac_test=frac_test,
groups=groups) #80/20% for all inlets
if ((dftrain.shape[0] + dftest.shape[0]) != df.shape[0]):
ValueError(
'The shapes of the resulting files are inconsistent with the shape of input table!'
)
else:
dftrain.to_csv(os.path.join(path, ftrain_name), index=False)
dftest.to_csv(os.path.join(path, ftest_name), index=False)
return dftrain, dftest
def create_loaders(dataset_name,
dataset_train,
dataset_val,
dataset_test,
train_size,
val_size,
batch_size,
test_batch_size,
cuda,
num_workers,
topk=None,
noise=False):
kwargs = {'num_workers': num_workers, 'pin_memory': True} if cuda else {}
dataset_train, dataset_val = split_dataset(dataset_train, dataset_val,
train_size, val_size)
print('Dataset sizes: \t train: {} \t val: {} \t test: {}'.format(
len(dataset_train), len(dataset_val), len(dataset_test)))
train_loader = data.DataLoader(dataset_train,
batch_size=batch_size,
shuffle=True,
**kwargs)
val_loader = data.DataLoader(dataset_val,
batch_size=test_batch_size,
shuffle=False,
**kwargs)
test_loader = data.DataLoader(dataset_test,
batch_size=test_batch_size,
shuffle=False,
**kwargs)
train_loader.tag = 'train'
val_loader.tag = 'val'
test_loader.tag = 'test'
return train_loader, val_loader, test_loader
def get_feats_from_csv_in_partitions():
"""
Extract the original features that are distributed in the dataset. Features
are splitted according with the config.yaml file.
"""
conf = utils.get_config()
rows = [row for row in utils.load_csv() if utils.check_filter(row, conf['filters'])]
train_rows, valid_rows, test_rows = utils.split_dataset(
rows, conf['valid_percent'], conf['test_percent'], rng=conf['rng_seed'])
X_train, y_train, X_valid, y_valid, X_test, y_test = [], [], [], [], [], []
prefixes = ['t_', 'i_', 's_'] # Feature names' prefixes
datasets = [(X_train, y_train, train_rows),
(X_test, y_test, test_rows), (X_valid, y_valid, valid_rows)]
out = []
for X, y, rows in datasets:
for row in rows:
X.append(
[float(v) for k, v in row.iteritems() if len(filter(k.startswith, prefixes)) > 0])
y.append(int(row['classification'] == 'Malign'))
out.extend((np.asarray(X), np.asarray(y)))
return out
def exercicio2():
utils.print_header(2)
data, classes = load_balance_scale(os.path.join(constants.DATA_DIR, constants.FILENAME_BALANCE_DATABASE))
print('Nb samples: {}'.format(data.shape[0]))
gaussian_accuracy, discrete_accuracy, laplace_accuracy = [], [], []
np.random.seed(constants.SEED)
for i in range(10):
x_train, y_train, x_test, y_test = utils.split_dataset(data)
params = {'mean': {}, 'std': {}, 'classes': classes, 'prior': {}, 'discrete_prob': {}}
for c in classes:
params['prior'][c] = sum(y_train == c) / float(x_train.shape[0])
x_c = x_train[y_train == c]
params['mean'][c] = np.mean(x_c, axis=0)
params['std'][c] = np.std(x_c, axis=0)
params['discrete_prob'][c] = {}
for j in range(x_c.shape[1]):
params['discrete_prob'][c][j] = {}
for k in [1, 2, 3, 4, 5]:
params['discrete_prob'][c][j][k] = {
'sum': sum(x_c[:, j] == k),
'n': x_c.shape[0],
}
gaussian_pred = gaussian_predict(x_test, params)
gaussian_accuracy.append(utils.accuracy(y_test, gaussian_pred))
discrete_pred = discrete_predict(x_test, params, laplace=False)
discrete_accuracy.append(utils.accuracy(y_test, discrete_pred))
laplace_pred = discrete_predict(x_test, params, laplace=True)
laplace_accuracy.append(utils.accuracy(y_test, laplace_pred))
print('a)')
print('\tGaussian - Accuracy: {:.2f} +- {:.2f}'.format(np.mean(gaussian_accuracy), np.std(gaussian_accuracy)))
print('b)')
print('\tDiscrete - Accuracy: {:.2f} +- {:.2f}'.format(np.mean(discrete_accuracy), np.std(discrete_accuracy)))
print('c)')
print('\tDiscrete (with Laplace) - Accuracy: {:.2f} +- {:.2f}'.format(np.mean(laplace_accuracy), np.std(laplace_accuracy)))
exit()
def main():
# Building Phase
data = import_data(
"./dataset/crx_clean.data.txt"
)
X, Y, X_train, X_test, Y_train, Y_test = split_dataset(data)
clf_entropy = train_using_entropy(X_train, Y_train)
# Operational Phase
print("\n### SINGLE TRAIN-TEST SPLIT ###\n")
Y_pred_entropy = prediction(X_test, clf_entropy)
print_scores(Y_test, Y_pred_entropy)
print("\n### CROSS VAL USING STRATIFIED K FOLD ###\n")
fold_scores = cv_with_entropy(X, Y)
print("Cross Validate: ", fold_scores)
print("Best F1_score: ", max(fold_scores)*100)
scorer = make_scorer(f1_score, pos_label='+')
print("\n### GRID SEARCH CROSS VAL USING STRATIFIED K FOLD###\n")
Y_pred_grid_search = grid_search_cv_DT(X_train, Y_train, X_test, Y_test, scorer)
print_scores(Y_test, Y_pred_grid_search)
def objective(params):
nneurons = params['nneurons']
if params['season'] == 'full_day':
window = create_window_array(params['window'], season_lag=288)
if params['season'] == 'half_day':
window = create_window_array(params['window'], season_lag=168)
else:
window = create_window_array(params['window'])
if not any(window) or nneurons < 2:
return {'status': STATUS_FAIL}
X_train, y_train, *_ = split_dataset(
data, window, ratio=0.90, standardize=True)
model = compile_model(
nneurons, input_dim=sum(1 for x in window if x), loss_fn='mse',
activation_fn=params['activation_function'])
hist = model.fit(
X_train, y_train, nb_epoch=50, validation_split=0.1,
callbacks=[EarlyStopping(monitor='val_loss', patience=2)],
verbose=0)
return {'loss': hist.history['val_loss'][-1], 'status': STATUS_OK}
def main(args):
####################
# Arguments
gpu = args.gpu
model_name = args.model
initial_tree_sampling = args.initial_tree_sampling
path_config = args.config
data_augmentation = args.data_augmentation
trial_name = args.name
actiontype = args.actiontype
max_epoch = args.max_epoch
dev_size = args.dev_size
# Check
assert actiontype in ["train", "evaluate"]
if actiontype == "train":
assert max_epoch > 0
assert len(initial_tree_sampling.split("_")) == 3
for type_ in initial_tree_sampling.split("_"):
assert type_ in ["X", "BU", "TD", "RB", "LB", "RB2"]
assert initial_tree_sampling.split("_")[2] != "X"
assert initial_tree_sampling.split("_")[1] != "RB2"
assert initial_tree_sampling.split("_")[2] != "RB2"
if trial_name is None or trial_name == "None":
trial_name = utils.get_current_time()
####################
# Path setting
config = utils.Config(path_config)
basename = "%s.%s.%s.aug_%s.%s" \
% (model_name,
initial_tree_sampling,
utils.get_basename_without_ext(path_config),
data_augmentation,
trial_name)
if actiontype == "train":
path_log = os.path.join(config.getpath("results"),
basename + ".training.log")
elif actiontype == "evaluate":
path_log = os.path.join(config.getpath("results"),
basename + ".evaluation.log")
path_train = os.path.join(config.getpath("results"),
basename + ".training.jsonl")
path_valid = os.path.join(config.getpath("results"),
basename + ".validation.jsonl")
path_snapshot = os.path.join(config.getpath("results"),
basename + ".model")
path_pred = os.path.join(config.getpath("results"),
basename + ".evaluation.ctrees")
path_eval = os.path.join(config.getpath("results"),
basename + ".evaluation.json")
utils.set_logger(path_log)
####################
# Random seed
random_seed = trial_name
random_seed = utils.hash_string(random_seed)
random.seed(random_seed)
np.random.seed(random_seed)
cuda.cupy.random.seed(random_seed)
####################
# Log so far
utils.writelog("gpu=%d" % gpu)
utils.writelog("model_name=%s" % model_name)
utils.writelog("initial_tree_sampling=%s" % initial_tree_sampling)
utils.writelog("path_config=%s" % path_config)
utils.writelog("data_augmentation=%s" % data_augmentation)
utils.writelog("trial_name=%s" % trial_name)
utils.writelog("actiontype=%s" % actiontype)
utils.writelog("max_epoch=%s" % max_epoch)
utils.writelog("dev_size=%s" % dev_size)
utils.writelog("path_log=%s" % path_log)
utils.writelog("path_train=%s" % path_train)
utils.writelog("path_valid=%s" % path_valid)
utils.writelog("path_snapshot=%s" % path_snapshot)
utils.writelog("path_pred=%s" % path_pred)
utils.writelog("path_eval=%s" % path_eval)
utils.writelog("random_seed=%d" % random_seed)
####################
# Data preparation
begin_time = time.time()
train_dataset = dataloader.read_rstdt("train",
relation_level="coarse-grained",
with_root=False)
test_dataset = dataloader.read_rstdt("test",
relation_level="coarse-grained",
with_root=False)
vocab_word = utils.read_vocab(
os.path.join(config.getpath("data"), "rstdt-vocab", "words.vocab.txt"))
vocab_postag = utils.read_vocab(
os.path.join(config.getpath("data"), "rstdt-vocab",
"postags.vocab.txt"))
vocab_deprel = utils.read_vocab(
os.path.join(config.getpath("data"), "rstdt-vocab",
"deprels.vocab.txt"))
if data_augmentation:
external_train_dataset = dataloader.read_ptbwsj_wo_rstdt(
with_root=False)
# Remove documents with only one leaf node
external_train_dataset = utils.filter_dataset(
external_train_dataset,
condition=lambda data: len(data.edu_ids) > 1)
end_time = time.time()
utils.writelog("Loaded the corpus. %f [sec.]" % (end_time - begin_time))
####################
# Hyper parameters
word_dim = config.getint("word_dim")
postag_dim = config.getint("postag_dim")
deprel_dim = config.getint("deprel_dim")
lstm_dim = config.getint("lstm_dim")
mlp_dim = config.getint("mlp_dim")
n_init_epochs = config.getint("n_init_epochs")
negative_size = config.getint("negative_size")
batch_size = config.getint("batch_size")
weight_decay = config.getfloat("weight_decay")
gradient_clipping = config.getfloat("gradient_clipping")
optimizer_name = config.getstr("optimizer_name")
utils.writelog("word_dim=%d" % word_dim)
utils.writelog("postag_dim=%d" % postag_dim)
utils.writelog("deprel_dim=%d" % deprel_dim)
utils.writelog("lstm_dim=%d" % lstm_dim)
utils.writelog("mlp_dim=%d" % mlp_dim)
utils.writelog("n_init_epochs=%d" % n_init_epochs)
utils.writelog("negative_size=%d" % negative_size)
utils.writelog("batch_size=%d" % batch_size)
utils.writelog("weight_decay=%f" % weight_decay)
utils.writelog("gradient_clipping=%f" % gradient_clipping)
utils.writelog("optimizer_name=%s" % optimizer_name)
####################
# Model preparation
cuda.get_device(gpu).use()
# Initialize a model
utils.mkdir(os.path.join(config.getpath("data"), "caches"))
path_embed = config.getpath("pretrained_word_embeddings")
path_caches = os.path.join(
config.getpath("data"), "caches",
"cached." + os.path.basename(path_embed) + ".npy")
if os.path.exists(path_caches):
utils.writelog("Loading cached word embeddings ...")
initialW = np.load(path_caches)
else:
initialW = utils.read_word_embedding_matrix(path=path_embed,
dim=word_dim,
vocab=vocab_word,
scale=0.0)
np.save(path_caches, initialW)
if model_name == "spanbasedmodel":
# Span-based model w/ template features
template_feature_extractor = models.TemplateFeatureExtractor(
dataset=train_dataset)
utils.writelog("Template feature size=%d" %
template_feature_extractor.feature_size)
if actiontype == "train":
for template in template_feature_extractor.templates:
dim = template_feature_extractor.template2dim[template]
utils.writelog("Template feature #%s %s" % (dim, template))
model = models.SpanBasedModel(
vocab_word=vocab_word,
vocab_postag=vocab_postag,
vocab_deprel=vocab_deprel,
word_dim=word_dim,
postag_dim=postag_dim,
deprel_dim=deprel_dim,
lstm_dim=lstm_dim,
mlp_dim=mlp_dim,
initialW=initialW,
template_feature_extractor=template_feature_extractor)
elif model_name == "spanbasedmodel2":
# Span-based model w/o template features
model = models.SpanBasedModel2(vocab_word=vocab_word,
vocab_postag=vocab_postag,
vocab_deprel=vocab_deprel,
word_dim=word_dim,
postag_dim=postag_dim,
deprel_dim=deprel_dim,
lstm_dim=lstm_dim,
mlp_dim=mlp_dim,
initialW=initialW)
else:
raise ValueError("Invalid model_name=%s" % model_name)
utils.writelog("Initialized the model ``%s''" % model_name)
# Load pre-trained parameters
if actiontype != "train":
serializers.load_npz(path_snapshot, model)
utils.writelog("Loaded trained parameters from %s" % path_snapshot)
model.to_gpu(gpu)
####################
# Decoder preparation
decoder = decoders.IncrementalCKYDecoder()
####################
# Initializer preparation
sampler = treesamplers.TreeSampler(initial_tree_sampling.split("_"))
####################
# Training / evaluation
if actiontype == "train":
with chainer.using_config("train", True):
if dev_size > 0:
# Training with cross validation
train_dataset, dev_dataset = utils.split_dataset(
dataset=train_dataset, n_dev=dev_size, seed=None)
with open(
os.path.join(config.getpath("results"),
basename + ".valid_gold.ctrees"),
"w") as f:
for data in dev_dataset:
f.write("%s\n" % " ".join(data.nary_sexp))
else:
# Training with the full training set
dev_dataset = None
if data_augmentation:
train_dataset = np.concatenate(
[train_dataset, external_train_dataset], axis=0)
train(model=model,
decoder=decoder,
sampler=sampler,
max_epoch=max_epoch,
n_init_epochs=n_init_epochs,
negative_size=negative_size,
batch_size=batch_size,
weight_decay=weight_decay,
gradient_clipping=gradient_clipping,
optimizer_name=optimizer_name,
train_dataset=train_dataset,
dev_dataset=dev_dataset,
path_train=path_train,
path_valid=path_valid,
path_snapshot=path_snapshot,
path_pred=os.path.join(config.getpath("results"),
basename + ".valid_pred.ctrees"),
path_gold=os.path.join(config.getpath("results"),
basename + ".valid_gold.ctrees"))
elif actiontype == "evaluate":
with chainer.using_config("train", False), chainer.no_backprop_mode():
# Test
parse(model=model,
decoder=decoder,
dataset=test_dataset,
path_pred=path_pred)
scores = metrics.rst_parseval(
pred_path=path_pred,
gold_path=os.path.join(config.getpath("data"), "rstdt", "wsj",
"test", "gold.labeled.nary.ctrees"))
old_scores = metrics.old_rst_parseval(
pred_path=path_pred,
gold_path=os.path.join(config.getpath("data"), "rstdt", "wsj",
"test", "gold.labeled.nary.ctrees"))
out = {
"Morey2018": {
"Unlabeled Precision": scores["S"]["Precision"] * 100.0,
"Precision_info": scores["S"]["Precision_info"],
"Unlabeled Recall": scores["S"]["Recall"] * 100.0,
"Recall_info": scores["S"]["Recall_info"],
"Micro F1": scores["S"]["Micro F1"] * 100.0
},
"Marcu2000": {
"Unlabeled Precision":
old_scores["S"]["Precision"] * 100.0,
"Precision_info": old_scores["S"]["Precision_info"],
"Unlabeled Recall": old_scores["S"]["Recall"] * 100.0,
"Recall_info": old_scores["S"]["Recall_info"],
"Micro F1": old_scores["S"]["Micro F1"] * 100.0
}
}
utils.write_json(path_eval, out)
utils.writelog(utils.pretty_format_dict(out))
utils.writelog("Done: %s" % basename)
def boston_housing():
"""
Trains algorithms for the boston housing dataset
"""
# boston housing dataset is available in the sklearn library
boston = load_boston()
dimension = boston.data.shape[1]
boston_data = np.column_stack((boston.data, boston.target))
split = 2 / 3
iterations = 20
gammas = np.linspace(math.pow(2, -40), math.pow(2, -26), 15)
sigmas = np.linspace(math.pow(2, 7), math.pow(2, 13), 14)
k_fold = 5
naive_results = np.zeros((iterations, 2))
single_results = np.zeros((iterations, dimension, 2))
all_results = np.zeros((iterations, 2))
k_results = np.zeros((iterations, 2))
for i in range(iterations):
print("iterations i", i)
training_set, testing_set = utils.split_dataset(boston_data, split)
# naive
_, train_mse, test_mse = lr.naive(training_set, testing_set)
naive_results[i, 0] = train_mse
naive_results[i, 1] = test_mse
# single attribute
for attr in range(dimension):
_, train_mse, test_mse = lr.single_attribute(training_set,
testing_set, attr)
single_results[i, attr, 0] = train_mse
single_results[i, attr, 1] = test_mse
# all attributes
_, train_mse, test_mse = lr.all_attributes(training_set, testing_set)
all_results[i, 0] = train_mse
all_results[i, 1] = test_mse
# kernel
_, train_mse, test_mse = rr.gaussian_kernel_cross_validation(
training_set, testing_set, gammas, sigmas, k_fold)
k_results[i, 0] = train_mse
k_results[i, 1] = test_mse
# display naive results
print("Naive MSE train: %s +- %s" % (
np.mean(naive_results[:, 0]), np.std(naive_results[:, 0])))
print("Naive MSE test: %s +- %s" % (
np.mean(naive_results[:, 1]), np.std(naive_results[:, 1])))
# display single attribute results
for attr in range(dimension):
id = attr + 1
print("Linear (attribute %d) MSE train: %s +- %s" % (
id,
np.mean(single_results[:, attr, 0]),
np.std(single_results[:, attr, 0])))
print("Linear (attribute %d) MSE test: %s +- %s" % (
id,
np.mean(single_results[:, attr, 1]),
np.std(single_results[:, attr, 1])))
# display all attributes results
print("Linear (all) MSE train: %s +- %s" % (
np.mean(all_results[:, 0]), np.std(all_results[:, 0])))
print("Linear (all) MSE test: %s +- %s" % (
np.mean(all_results[:, 1]), np.std(all_results[:, 1])))
# display kernel results
print("Kernel MSE train: %s +- %s" % (
np.mean(k_results[:, 0]), np.std(k_results[:, 0])))
print("Kernel MSE test: %s +- %s" % (
np.mean(k_results[:, 1]), np.std(k_results[:, 1])))
self.__get_labels_neighborhood(row, distance)
for row in X_classifier
]
if __name__ == "__main__":
PATH_FILE = (
"/home/nobrega/Dados/Documentos/Estudos/notes/dataset/knn_classification.csv"
)
df = pd.read_csv(PATH_FILE)
df.pop("id")
y = df["class"].values
y = y.reshape(len(y), 1)
df.pop("class")
X = df.values
X_train, y_train, X_test, y_test = split_dataset(X, y, 0.7)
n_neighbors = 15
knn = KNearestNeighbors(X_train, y_train, n_neighbors)
for type_distance in DistanceTypes:
try:
y_hat = np.array(knn.predict(X_test, type_distance.value))
y_test = np.ndarray.flatten(np.array(y_test))
acc = sum(((y_hat == y_test) * 1.0)) / len(y_test)
print(f"Type of Distance {type_distance.value} Acurácia: {acc}")
except:
print(f"This distance type {type_distance.value} can't calculate")
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
keep_topK = 200
USE_HOLD_OUT = True # visualization of HOLD-OUT set
# dir_prenms = "../results/preNMS"
# =========================== Dataset ==============================
# file path and make a list
imgs_path = '../data/hw3_mycocodata_img_comp_zlib.h5'
masks_path = '../data/hw3_mycocodata_mask_comp_zlib.h5'
labels_path = "../data/hw3_mycocodata_labels_comp_zlib.npy"
bboxes_path = "../data/hw3_mycocodata_bboxes_comp_zlib.npy"
paths = [imgs_path, masks_path, labels_path, bboxes_path]
dataset = BuildDataset(paths, augmentation=False)
train_dataset, test_dataset = utils.split_dataset(dataset)
# dataset
# train_build_loader = BuildDataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=8)
# train_loader = train_build_loader.loader()
test_build_loader = BuildDataLoader(test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=0)
test_loader = test_build_loader.loader()
# we will need the ImageList from torchvision
from torchvision.models.detection.image_list import ImageList
do_eval(test_loader,
checkpoint_file,
def main():
#torch.manual_seed(42)
# ------------
# args
# ------------
parser = ArgumentParser()
parser.add_argument('--auto_lr',
type=U.str2bool,
default=False,
help="Auto lr finder")
parser.add_argument('--learning_rate', type=float, default=10e-4)
parser.add_argument('--scheduler', type=U.str2bool, default=False)
parser.add_argument('--wd', type=float, default=2e-4)
parser.add_argument('--moment', type=float, default=0.9)
parser.add_argument('--batch_size', default=5, type=int)
parser.add_argument('--n_epochs', default=10, type=int)
parser.add_argument('--model',
default='FCN',
type=str,
help="FCN or DLV3 model")
parser.add_argument('--pretrained',
default=False,
type=U.str2bool,
help="Use pretrained pytorch model")
parser.add_argument('--eval_angle', default=True, type=U.str2bool,help=\
"If true, it'll eval the model with different angle input size")
parser.add_argument('--rotate',
default=False,
type=U.str2bool,
help="Use random rotation as data augmentation")
parser.add_argument('--scale',
default=True,
type=U.str2bool,
help="Use scale as data augmentation")
parser.add_argument('--size_img',
default=520,
type=int,
help="Size of input images")
parser.add_argument('--size_crop',
default=480,
type=int,
help="Size of crop image during training")
parser.add_argument('--nw',
default=0,
type=int,
help="Num workers for the data loader")
parser.add_argument('--pm',
default=True,
type=U.str2bool,
help="Pin memory for the dataloader")
parser.add_argument('--gpu',
default=0,
type=int,
help="Wich gpu to select for training")
parser.add_argument('--benchmark',
default=False,
type=U.str2bool,
help="enable or disable backends.cudnn")
parser.add_argument('--split',
default=False,
type=U.str2bool,
help="Split the dataset")
parser.add_argument('--split_ratio',
default=0.3,
type=float,
help="Amount of data we used for training")
parser.add_argument('--dataroot_voc',
default='/share/DEEPLEARNING/datasets/voc2012/',
type=str)
parser.add_argument('--dataroot_sbd',
default='/share/DEEPLEARNING/datasets/sbd/',
type=str)
parser.add_argument('--model_name',
type=str,
help="what name to use for saving")
parser.add_argument('--save_dir', default='/data/save_model', type=str)
parser.add_argument('--save_all_ep', default=False, type=U.str2bool,help=\
"If true it'll save the model every epoch in save_dir")
parser.add_argument('--save_best',
default=False,
type=U.str2bool,
help="If true will only save the best epoch model")
args = parser.parse_args()
# ------------
# save
# ------------
save_dir = U.create_save_directory(args.save_dir)
print('model will be saved in', save_dir)
U.save_hparams(args, save_dir)
# ------------
# device
# ------------
device = torch.device(
"cuda:" + str(args.gpu) if torch.cuda.is_available() else "cpu")
print("device used:", device)
# ------------
# model
# ------------
if args.model.upper() == 'FCN':
model = models.segmentation.fcn_resnet101(pretrained=args.pretrained)
elif args.model.upper() == 'DLV3':
model = models.segmentation.deeplabv3_resnet101(
pretrained=args.pretrained)
else:
raise Exception('model must be "FCN" or "DLV3"')
model.to(device)
# ------------
# data
# ------------
if args.size_img < args.size_crop:
raise Exception(
'Cannot have size of input images less than size of crop')
size_img = (args.size_img, args.size_img)
size_crop = (args.size_crop, args.size_crop)
train_dataset_VOC = mdset.VOCSegmentation(args.dataroot_voc,year='2012', image_set='train', \
download=True,rotate=args.rotate,scale=args.scale,size_img=size_img,size_crop=size_crop)
val_dataset_VOC = mdset.VOCSegmentation(args.dataroot_voc,
year='2012',
image_set='val',
download=True)
train_dataset_SBD = mdset.SBDataset(args.dataroot_sbd, image_set='train_noval',mode='segmentation',\
rotate=args.rotate,scale=args.scale,size_img=size_img,size_crop=size_crop)
# Concatene dataset
train_dataset = tud.ConcatDataset([train_dataset_VOC, train_dataset_SBD])
split = args.split
if split == True:
train_dataset = U.split_dataset(train_dataset, args.split_ratio)
# Print len datasets
print("There is", len(train_dataset), "images for training and",
len(val_dataset_VOC), "for validation")
dataloader_train = torch.utils.data.DataLoader(train_dataset, batch_size=args.batch_size,num_workers=args.nw,\
pin_memory=args.pm,shuffle=True,drop_last=True)#,collate_fn=U.my_collate)
dataloader_val = torch.utils.data.DataLoader(val_dataset_VOC,num_workers=args.nw,pin_memory=args.pm,\
batch_size=args.batch_size)
# Decide which device we want to run on
# ------------
# training
# ------------
# Auto lr finding
#if args.auto_lr==True:
criterion = nn.CrossEntropyLoss(
ignore_index=21) # On ignore la classe border.
optimizer = torch.optim.SGD(model.parameters(),
lr=args.learning_rate,
momentum=args.moment,
weight_decay=args.wd)
ev.train_fully_supervised(model=model,n_epochs=args.n_epochs,train_loader=dataloader_train,val_loader=dataloader_val,\
criterion=criterion,optimizer=optimizer,save_folder=save_dir,scheduler=args.scheduler,model_name=args.model_name,\
benchmark=args.benchmark, save_best=args.save_best,save_all_ep=args.save_all_ep,device=device,num_classes=21)
# Final evaluation
if args.eval_angle:
d_iou = ev.eval_model_all_angle(model,
args.size_img,
args.dataroot_voc,
train=True,
device=device)
U.save_eval_angle(d_iou, save_dir)
d_iou = ev.eval_model_all_angle(model,
args.size_img,
args.dataroot_voc,
train=False,
device=device)
U.save_eval_angle(d_iou, save_dir)
def train_dvna(A, P, verbose=False):
n_epochs = 4000
train_ones_indices, train, val, test = u.split_dataset(A, seed=seed)
A_train = u.prepare_train_matrix_dvne(A, train_ones_indices)
# sample triplets
# nbrs is a dict nbrs[i] -> {j1,j2,...,}
nbrs = {}
not_nbrs = {}
all_nodes = {i for i in range(A.shape[0])}
for ij in zip(train_ones_indices[0], train_ones_indices[1]):
i, j = int(ij[0]), int(ij[1])
if i in nbrs.keys():
nbrs[i] = nbrs[i].union({j})
else:
nbrs[i] = {j}
if j in nbrs.keys():
nbrs[j] = nbrs[j].union({i})
else:
nbrs[j] = {i}
for i in nbrs.keys():
nbrs_set = nbrs[i]
not_nbrs[i] = all_nodes - nbrs_set
model = DVNE(n_features=A.shape[0])
model.to(device)
opt = torch.optim.Adam(lr=0.001, params=model.parameters())
nonzero_ratio = A.sum() / (A.shape[0]**2)
zero_ratio = 1 - nonzero_ratio
A = A.to(device)
P = P.to(device)
A_train = A_train.to(device)
model.n_samples = len(train)
criterion = l.dvne_loss
for e in range(n_epochs):
t0 = time.time()
triplets = u.sample_triplets(nbrs, not_nbrs, 300)
i, j, k = triplets
model.train()
opt.zero_grad()
out_i, mi, stdi = model.forward(A_train[i, :])
out_j, mj, stdj = model.forward(A_train[j, :])
out_k, mk, stdk = model.forward(A_train[k, :])
gt_i = P[i, :]
gt_j = P[j, :]
gt_k = P[k, :]
out_reconstruction = torch.cat([out_i, out_j, out_k], dim=0).view(-1)
gt = torch.cat([gt_i, gt_j, gt_k], dim=0).view(-1).to(device)
a_gt = torch.cat([A[i, :], A[j, :], A[j, :]],
dim=0).view(-1).to(device)
x = torch.ones(gt.shape[0]).to(device)
weights = torch.where(gt == 0.0, x * float(nonzero_ratio),
x * float(zero_ratio)).to(device)
loss_norm = weights.sum() / len(train)
loss_weight = 0.6
# loss = criterion(out_reconstruction, gt_reconsturction, weight=weights) * loss_weight/ loss_norm
l2 = criterion(gt, out_reconstruction) * loss_weight
# loss = 0.0
w_ij = model.wasserstein((mi, stdi), (mj, stdj))
w_ik = model.wasserstein((mi, stdi), (mk, stdk))
l1 = l.energy_loss(w_ij, w_ik)
loss = l1 + loss_weight * l2
loss.backward()
opt.step()
t1 = time.time()
if verbose:
if (e + 1) % 100 == 0:
if len(val) > 0:
with torch.no_grad():
val_loss = float(
criterion(
(model.forward(A_train)[0]).reshape(-1)[val],
A.reshape(-1)[val].data))
val_auc = u.test_auc_dvna(model, A_train, A, val)
else:
val_auc = np.nan
val_loss = np.nan
print(
"Iteration: {0}; train loss: {1:.4f}; val loss: {2:.4f}; val auc: {3:.4f}; time: {4:.4f}"
.format(e + 1, loss, val_loss, val_auc, t1 - t0))
test_auc = u.test_auc_dvna(model, A_train, A, idx=test, test=True)
# print("Test auc: ", test_auc)
if dataset_name == 'cora' and visualize:
with torch.no_grad():
encodings, mean, std = model.encode(P.to(device))
embeddings = torch.cat([mean, std], dim=1)
dv.reduct_and_visualize(embeddings.cpu().numpy(), Y.argmax(axis=1))
train, val_test = next(
Split(train_size=140, random_state=seed).split(embeddings, Y))
embeddings = embeddings.cpu()
x_train, y_train = embeddings[train], Y[train]
x_test, y_test = embeddings[val_test], Y[val_test]
svm = SVC(C=10.0)
svm.fit(x_train, y_train.argmax(axis=1))
y_predicted = svm.predict(x_test)
print("SVM Accuracy: ",
accuracy_score(y_predicted, y_test.argmax(axis=1)))
return test_auc
def run(self, id, updated_fields, enable_service=False):
share = self.datastore.get_by_id('shares', id)
if not share:
raise TaskException(errno.ENOENT, 'Share not found')
if share['immutable']:
raise TaskException(errno.EACCES, 'Cannot modify immutable share {0}.'.format(id))
if 'name' in updated_fields or 'type' in updated_fields:
share.update(updated_fields)
if self.datastore.exists(
'shares',
('id', '!=', id),
('type', '=', share['type']),
('name', '=', share['name'])
):
raise TaskException(errno.EEXIST, 'Share {0} of type {1} already exists'.format(
share['name'],
share['type']
))
path_after_update = updated_fields.get('target_path', share['target_path'])
type_after_update = updated_fields.get('target_type', share['target_type'])
permissions = updated_fields.pop('permissions', None)
share_path = self.dispatcher.call_sync('share.expand_path', path_after_update, type_after_update)
if not os.path.exists(share_path):
raise TaskException(
errno.ENOENT,
'Selected share target {0} does not exist'.format(path_after_update)
)
share = self.datastore.get_by_id('shares', id)
remove_unchanged(updated_fields, share)
path = self.dispatcher.call_sync('share.get_directory_path', share['id'])
try:
delete_config(
path,
'{0}-{1}'.format(share['type'], share['name'])
)
except (OSError, ValueError):
pass
if 'type' in updated_fields:
old_share_type = share['type']
new_share_type = self.dispatcher.call_sync('share.supported_types').get(updated_fields['type'])
if share['target_type'] == 'DATASET':
pool, dataset = split_dataset(share['target_path'])
self.join_subtasks(
self.run_subtask('volume.dataset.update', dataset, {
'permissions_type': new_share_type['perm_type']
})
)
share.update(updated_fields)
self.run_subtask_sync('share.{0}.delete'.format(old_share_type), id)
self.run_subtask_sync('share.{0}.create'.format(updated_fields['type']), share)
else:
self.run_subtask_sync('share.{0}.update'.format(share['type']), id, updated_fields)
if permissions:
path = self.dispatcher.call_sync('share.translate_path', id)
self.run_subtask_sync('file.set_permissions', path, permissions)
self.dispatcher.dispatch_event('share.changed', {
'operation': 'update',
'ids': [share['id']]
})
updated_share = self.datastore.get_by_id('shares', id)
path = self.dispatcher.call_sync('share.get_directory_path', updated_share['id'])
try:
save_config(
path,
'{0}-{1}'.format(updated_share['type'], updated_share['name']),
updated_share
)
except OSError as err:
self.add_warning(TaskWarning(errno.ENXIO, 'Cannot save backup config file: {0}'.format(str(err))))
service_state = self.dispatcher.call_sync('service.query', [('name', '=', share['type'])], {'single': True})
if service_state['state'] != 'RUNNING':
if enable_service:
config = service_state['config']
config['enable'] = True
self.run_subtask_sync('service.update', service_state['id'], {'config': config})
else:
self.add_warning(TaskWarning(
errno.ENXIO, "Share has been updated but the service {0} is not currently running "
"Please enable the {0} service.".format(share['type'])
))
def run(self, id, updated_fields, enable_service=False):
share = self.datastore.get_by_id('shares', id)
if not share:
raise TaskException(errno.ENOENT, 'Share not found')
if share['immutable']:
raise TaskException(errno.EACCES, 'Cannot modify immutable share {0}.'.format(id))
if 'name' in updated_fields or 'type' in updated_fields:
share.update(updated_fields)
if self.datastore.exists(
'shares',
('id', '!=', id),
('type', '=', share['type']),
('name', '=', share['name'])
):
raise TaskException(errno.EEXIST, 'Share {0} of type {1} already exists'.format(
share['name'],
share['type']
))
path_after_update = updated_fields.get('target_path', share['target_path'])
type_after_update = updated_fields.get('target_type', share['target_type'])
permissions = updated_fields.pop('permissions', None)
share_path = self.dispatcher.call_sync('share.expand_path', path_after_update, type_after_update)
if type_after_update in ('DIRECTORY', 'FILE'):
pool_mountpoints = tuple(self.dispatcher.call_sync('volume.query', [], {'select': 'mountpoint'}))
if not path_after_update.startswith(pool_mountpoints):
raise TaskException(errno.EINVAL, "Provided directory or file has to reside within user defined ZFS pool")
if not os.path.exists(share_path):
raise TaskException(
errno.ENOENT,
'Selected share target {0} does not exist'.format(path_after_update)
)
share = self.datastore.get_by_id('shares', id)
remove_unchanged(updated_fields, share)
path = self.dispatcher.call_sync('share.get_directory_path', share['id'])
try:
delete_config(
path,
'{0}-{1}'.format(share['type'], share['name'])
)
except (OSError, ValueError):
pass
if 'type' in updated_fields:
old_share_type = share['type']
new_share_type = self.dispatcher.call_sync('share.supported_types').get(updated_fields['type'])
if share['target_type'] == 'DATASET':
pool, dataset = split_dataset(share['target_path'])
self.join_subtasks(
self.run_subtask('volume.dataset.update', dataset, {
'permissions_type': new_share_type['perm_type']
})
)
share.update(updated_fields)
self.run_subtask_sync('share.{0}.delete'.format(old_share_type), id)
self.run_subtask_sync('share.{0}.create'.format(updated_fields['type']), share)
else:
self.run_subtask_sync('share.{0}.update'.format(share['type']), id, updated_fields)
if permissions:
path = self.dispatcher.call_sync('share.translate_path', id)
self.run_subtask_sync('file.set_permissions', path, permissions)
self.dispatcher.dispatch_event('share.changed', {
'operation': 'update',
'ids': [share['id']]
})
updated_share = self.datastore.get_by_id('shares', id)
path = self.dispatcher.call_sync('share.get_directory_path', updated_share['id'])
try:
save_config(
path,
'{0}-{1}'.format(updated_share['type'], updated_share['name']),
updated_share,
file_perms=0o600
)
except OSError as err:
self.add_warning(TaskWarning(errno.ENXIO, 'Cannot save backup config file: {0}'.format(str(err))))
service_state = self.dispatcher.call_sync('service.query', [('name', '=', share['type'])], {'single': True})
if service_state['state'] != 'RUNNING':
if enable_service:
config = service_state['config']
config['enable'] = True
self.run_subtask_sync('service.update', service_state['id'], {'config': config})
else:
self.add_warning(TaskWarning(
errno.ENXIO, "Share has been updated but the service {0} is not currently running "
"Please enable the {0} service.".format(share['type'])
))
import utils
from pylearn2.utils import serial
import h5py
import numpy as np
import sys
if __name__ == "__main__":
conf_file = sys.argv[1] if len(sys.argv) > 1 else None
conf = utils.get_config(conf_file)
paths = utils.get_paths()
region_size = conf['region_size']
region_stride = conf['region_stride']
train_rows, valid_rows, test_rows = utils.split_dataset(
utils.get_filtered_rows(), conf['valid_percent'],
conf['test_percent'], rng=conf['rng_seed'])
rowsdict = {'train': train_rows, 'valid': valid_rows, 'test': test_rows}
nsamples = {}
prefixes = ['s_', 'i_', 't_'] # Feature names' prefixes
for subset, subrows in rowsdict.iteritems():
X = None
y = []
feats = []
for row in subrows:
samples = utils.get_samples_from_image(
row, oversampling=(subset == 'train' and conf['oversampling']))
print "%i samples to %s taken from %s" % (
len(samples), subset, row['image_filename'])
if len(samples) == 0:
from torchvision import transforms
import os
import argparse
from utils import split_dataset, train, validate
from grayscale import Grayscale
from colorize import Colorize
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="path to input image")
ap.add_argument("-e", "--epochs", type=int, default=100, help="# of epochs")
args = vars(ap.parse_args())
# split the landscape dataset to train and validation folders
split_dataset(args["image"])
# Training
train_transforms = transforms.Compose(
[transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip()])
train_folder = Grayscale('images/train', train_transforms)
train_loader = torch.utils.data.DataLoader(train_folder,
batch_size=64,
shuffle=True)
# Validation
val_transforms = transforms.Compose(
[transforms.Resize(256),
transforms.CenterCrop(224)])
val_folder = Grayscale('images/val', val_transforms)
for i in range(0, len(rows), chunkSize):
offset = min(i + chunkSize, len(rows))
f_chunk, y_chunk = fe_extraction.get_feats_from_cnn(
rows[i:offset], model)
if feats is None:
feats = f_chunk
y = y_chunk
else:
feats = np.vstack((feats, f_chunk))
y = np.hstack((y, y_chunk))
segm_ids = np.asarray([int(row['segmentation_id']) for row in rows])
features = np.hstack((features, feats))
train_rows, valid_rows, test_rows = utils.split_dataset(
utils.get_filtered_rows(), conf['valid_percent'],
conf['test_percent'], rng=conf['rng_seed'])
rows = train_rows + valid_rows
patients = utils.rows_to_patients(rows)
for i in range(n_runs):
train_rows, empty_rows, valid_rows = utils.split_dataset(
rows, valid_percent=0, test_percent=0.2, rng=rng, patients=patients)
X_train, y_train = get_features(train_rows, features, segm_ids)
X_valid, y_valid = get_features(valid_rows, features, segm_ids)
print 'train: %i, valid: %i' % (X_train.shape[0], X_valid.shape[0])
if scale_feats:
min_max_scaler = preprocessing.MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
X_valid = min_max_scaler.transform(X_valid)
