def main(args=sys.argv[1:]):
args = parse_args(args)
logging.basicConfig(level=args.logging)
device = torch.device(
"cpu" if args.no_cuda or not torch.cuda.is_available() else "cuda")
vocab = Vocab()
# Load data now to know the whole vocabulary when training model.
train_data = data_loader.load(data_loader.path("train"), vocab)
valid_data = data_loader.load(data_loader.path("valid"), vocab)
test_data = data_loader.load(data_loader.path("test"), vocab)
model = RnnLm(len(vocab), args.embedding_dim, args.gru_hidden,
args.gru_layers, not args.untied,
args.gru_dropout).to(device)
optimizer = optim.RMSprop(model.parameters(), lr=args.lr)
for epoch_ind in range(args.epochs):
logging.info("Training epoch %d", epoch_ind)
train_epoch(train_data, model, optimizer, args, device)
logging.info("Validation perplexity: %.1f",
evaluate(valid_data, model, args.batch_size, device))
logging.info("Test perplexity: %.1f",
evaluate(test_data, model, args.batch_size, device))
def run(training_data, test_data, problog_file):
queries = load(training_data)
test_queries = load(test_data)
network = MultiLabelNet()
with open(problog_file, 'r') as f:
problog_string = f.read()
net = Network(network, 'multilabel_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model = Model(problog_string, [net], caching=False)
optimizer = Optimizer(model, 2)
train_model(
model,
queries,
nr_epochs=50,
optimizer=optimizer,
test_iter=len(queries) * 10,
# test=multilabel_test,
log_iter=500,
snapshot_iter=len(queries))
for query in test_queries:
print(query)
for k, v in model.solve(query).items():
print('\t{}: {:.4f}\t{}'.format(
k.args[1], v[0],
CLASSES_BY_LABEL[int(query.args[0])][str(k.args[1])]))
def main(config):
prepare_dirs_and_logger(config)
rng = np.random.RandomState(config.random_seed)
tf.set_random_seed(config.random_seed)
load()
train_data_loader, train_label_loader, train_loc_loader, train_mask_loader = get_loader(
config.data_path, config.batch_size, 0, 'train', True)
test_data_loader, test_label_loader, test_loc_loader, test_mask_loader = get_loader(
config.data_path, config.batch_size_test, 5, 'train', True)
trainer = Trainer(config, train_data_loader, train_label_loader,
train_loc_loader, train_mask_loader, test_data_loader,
test_label_loader, test_loc_loader, test_mask_loader)
print("loaded trainer")
if config.is_train:
save_config(config)
trainer.train()
print("finished train")
else:
if not config.load_path:
raise Exception(
"[!] You should specify `load_path` to load a pretrained model"
)
trainer.test()
def run_linear(training_data,
test_data,
problog_files,
problog_train_files=(),
problog_test_files=()):
queries = load(training_data)
test_queries = load(test_data)
# network = SoundLinearNet()
# network = SoundCNNet()
network = SoundVGGish()
problog_string = add_files_to(problog_files, '')
problog_train_string = add_files_to(problog_train_files, problog_string)
problog_test_string = add_files_to(problog_test_files, problog_string)
net = Network(network, 'sound_net', neural_predicate_vggish)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model_to_train = Model(problog_train_string, [net], caching=False)
optimizer = Optimizer(model_to_train, 2)
model_to_test = Model(problog_test_string, [net], caching=False)
train_model(model_to_train,
queries,
nr_epochs=10,
optimizer=optimizer,
test_iter=len(queries),
test=lambda _: my_test(model_to_test, test_queries),
log_iter=500,
snapshot_iter=len(queries),
snapshot_name=' SequenceDetectionSnapshots/model')
def predict():
"""
An example of how to load a trained model and use it
to predict labels.
"""
# load the saved model
classifier = cPickle.load(open('best_model.pkl'))
# compile a predictor function
predict_model = theano.function(
inputs=[classifier.input],
outputs=classifier.y_pred)
# We can test it on some examples from test test
dataset='data/mnist.pkl.gz'
training_set, validation_set, testing_set, = data_loader.load(dataset)
testing_set_x , testing_set_y = testing_set
testing_set_x = testing_set_x.get_value()
testing_set_y = testing_set_y.eval()[:30]
predicted_values = predict_model(testing_set_x[:30])
print ("Predicted values for the first 10 examples in test set:")
print predicted_values
print ("answers:")
print
def main():
tf.set_random_seed(2018)
dtrain, dtest, z, y_std = data_loader.load('airplane.csv', n_clusters=config.clusters, n_induce=config.num_inducing, sgp=config.sgp)
N, _ = dtrain.shape
model = VBSGPR(N, config.log_beta, config.log_sf2, config.log_theta, z, whiten=True)
clusters = [i for i in range(config.clusters)]
lb = model.lower_bound()
fmu, fcov = model.predict_f()
gp_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, 'vbsgpr')
gp_opt = tf.train.AdamOptimizer(0.01, beta1=0.9, name='gp_opt') # Best: 40.459
# gp_opt = tf.train.MomentumOptimizer(0.01, momentum=0.9, use_nesterov=False)
gp_train_op = gp_opt.minimize(-lb, var_list=gp_vars)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range(config.epochs):
random.shuffle(clusters)
for i, cluster in enumerate(clusters):
data_batch = dtrain[np.where(dtrain[:, -1] == cluster)]
X, y = data_batch[:, :-2], data_batch[:, -2:-1]
_, lb_ = sess.run([gp_train_op, lb], {model.x: X, model.y: y, model.batch: y.shape[0]})
if i % 100 == 0:
print ('Epoch: [{}], The {}-th Cluster: [{}], Lower Bound: [{}]'.format(
epoch, i, cluster, lb_))
X_test, y_test = dtest[:, :-2], dtest[:, -2:-1]
f_test, _ = sess.run([fmu, fcov], {model.x: X_test})
rmse = np.sqrt(np.mean(y_std**2 * ((y_test - f_test))**2))
print ('Epoch {} test RMSE: {}'.format(epoch, rmse))
def main():
# Select chain and info file with a GUI.
# datafile = open_file_gui(add_pattern="*.txt")
# infofile = open_file_gui(add_pattern="*.txt")
parser = arg_parser(description='Superplot summary tool', conflict_handler='resolve')
parser.add_argument('--data_file',
'-d',
help='Chain file to summarise',
type=str,
required=True)
parser.add_argument('--info_file',
'-i',
help='Info file to summarise',
type=str,
default=None,
required=False)
args = vars(parser.parse_args())
datafile = os.path.abspath(args['data_file'])
infofile = args['info_file']
if infofile:
infofile = os.path.abspath(infofile)
# Load and label data
labels, data = data_loader.load(infofile, datafile)
summary_table = _summary_table(labels, data, datafile=datafile, infofile=infofile)
return summary_table
def train(train_data, ep, bz):
x, y = data_loader.load(train_data, dim, t)
print('Training model ...')
model = create_model()
model.fit(x, y, epochs=ep, batch_size=bz, verbose=2)
# save(directory + 'model/', m_name, model)
evaluate(create_model, seed, x, y, t, ep, bz)
def run_coauthor(fold_i):
def neural_predicate(network, i, dataset='train'):
i = int(i)
dataset = str(dataset)
if dataset == 'train':
d, l = mnist_train_data[i]
elif dataset == 'test':
d, l = mnist_test_data[i]
d = Variable(d.unsqueeze(0))
output = network.net(d)
return output.squeeze(0)
queries = load('train_data.txt')
with open('coauthor_rules.pl') as f:
problog_string = f.read()
network = coauthor_net()
net = Network(network, f'coauthor {i+1}', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model = Model(problog_string, [net], caching=False)
optimizer = Optimizer(model, 2)
train_model(model,
queries,
1,
optimizer,
test_iter=1000,
test=test_coauthor,
snapshot_iter=10000)
def run(data_dir: str,
save_estimator=False,
segment_size=query_name_learner.SEGMENT_SIZE,
overlap: float = query_name_learner.OVERLAP_FRACTION,
is_segment_size_in_seconds: bool = False,
ipython_when_done=False):
data_loader.load(data_dir,
['frame_time_relative', 'dns_qry_name', 'dns_qry_type'])
query_name_learner.SEGMENT_SIZE = segment_size
query_name_learner.OVERLAP_FRACTION = overlap
query_name_learner.IS_SEGMENT_SIZE_IN_SECONDS = is_segment_size_in_seconds
per_user_states = query_name_learner.run_one_v_all(save_estimator)
if ipython_when_done:
from IPython import embed
embed()
def main():
df = data_loader.make_df("../input_data/data_h27v07")
df["mean_lai"] = df.path.map(
lambda x: np.mean(data_loader.load(x)["Lai_500m"]))
df.sort_values("date", ascending=True).plot(x="date", y="mean_lai")
plt.show()
def run(training_data,
test_data,
problog_files,
problog_train_files=(),
problog_test_files=(),
config_file=None,
net_mode='init',
cfg=None):
config = json.load(open(config_file))
config['net_mode'] = net_mode
config['cfg'] = cfg
queries = load(training_data)
test_queries = load(test_data)
sounds = SoundsUtils(config)
problog_string = add_files_to(problog_files, '')
problog_train_string = add_files_to(problog_train_files, problog_string)
problog_test_string = add_files_to(problog_test_files, problog_string)
network = sounds.network
net = Network(network, 'sound_net', sounds.neural_predicate)
net.optimizer = sounds.optimizer
model_to_train = Model(problog_train_string, [net], caching=False)
optimizer = Optimizer(model_to_train, 2)
model_to_test = Model(problog_test_string, [net], caching=False)
train_model(model_to_train,
queries,
5,
optimizer,
test_iter=len(queries),
test=lambda _: my_test(
model_to_test,
test_queries,
test_functions={
'sound_net':
lambda *args, **kwargs: sounds.neural_predicate(
*args, **kwargs, in_training=False)
},
),
snapshot_iter=len(queries))
def main():
# Select chain and info file with a GUI.
datafile = open_file_gui()
infofile = open_file_gui()
# Load and label data
labels, data = data_loader.load(infofile, datafile)
summary_table = _summary_table(labels, data, datafile=datafile, infofile=infofile)
return summary_table
def main():
# Select chain and info file with a GUI.
datafile = open_file_gui(add_pattern="*.txt")
infofile = open_file_gui(add_pattern="*.txt")
# Load and label data
labels, data = data_loader.load(infofile, datafile)
summary_table = _summary_table(labels, data, datafile=datafile, infofile=infofile)
return summary_table
def load_data(args):
data = data_loader.load(args.dataset,
n_train=args.n_train,
n_test=args.n_test,
train_noise=args.train_noise,
test_noise=args.test_noise)
stratify = args.dataset not in ["abalone", "segment"]
if args.dataset not in [
'arcene', 'moon', 'toy_Story', 'toy_Story_ood', 'segment'
]:
print(args.dataset)
x = data_loader.prepare_inputs(data['features'])
y = data['labels']
x_train, x_test, y_train, y_test = train_test_split(
x,
y,
train_size=args.train_test_ratio,
stratify=y if stratify else None)
else:
if args.dataset == 'moon' or args.dataset=='toy_Story' or \
args.dataset=='toy_Story_ood':
x_train, x_test = data['x_train'], data['x_val']
else:
x_train, x_test = data_loader.prepare_inputs(
data['x_train'], data['x_val'])
y_train, y_test = data['y_train'], data['y_val']
# Generate validation split
x_train, x_val, y_train, y_val = train_test_split(
x_train,
y_train,
train_size=args.train_test_ratio,
stratify=y_train if stratify else None)
x_train = x_train.astype(np.float32)
x_val = x_val.astype(np.float32)
x_test = x_test.astype(np.float32)
n_mean = np.mean(x_train, axis=0)
n_std = np.var(x_train, axis=0)**.5
x_train = (x_train - n_mean) / n_std
x_val = (x_val - n_mean) / n_std
x_test = (x_test - n_mean) / n_std
try:
if args.n_ood > 0 and y_val.shape[1] > args.n_ood:
n_ood = y_val.shape[1] - args.n_ood - 1
return utils.prepare_ood(x_train, x_val, x_test, y_train, y_val,
y_test, n_ood, args.norm)
except AttributeError:
#print(x_train, x_val, x_test, y_train, y_val, y_test)
return x_train, x_val, x_test, y_train, y_val, y_test, 0, 0
return x_train, x_val, x_test, y_train, y_val, y_test, 0, 0
def run(training_data, test_data, problog_files):
queries = load(training_data)
test_queries = load(test_data)
problog_string = ''
for problog_file in problog_files:
with open(problog_file) as f:
problog_string += f.read()
problog_string += '\n\n'
network = MNIST_Net()
net = Network(network, 'mnist_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model = Model(problog_string, [net], caching=False)
optimizer = Optimizer(model, 2)
train_model(model,
queries,
1,
optimizer,
test_iter=1000,
test=test_MNIST,
snapshot_iter=10000)
def run(training_data,
test_data,
problog_files,
problog_train_files=(),
problog_test_files=()):
queries = load(training_data)
test_queries = load(test_data)
problog_string = add_files_to(problog_files, '')
problog_train_string = add_files_to(problog_train_files, problog_string)
problog_test_string = add_files_to(problog_test_files, problog_string)
network = MNIST_Net()
net = Network(network, 'mnist_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model_to_train = Model(problog_train_string, [net], caching=False)
optimizer = Optimizer(model_to_train, 2)
model_to_test = Model(problog_test_string, [net], caching=False)
train_model(model_to_train,
queries,
1,
optimizer,
test_iter=len(queries),
test=lambda _: my_test(
model_to_test,
test_queries,
test_functions={
'mnist_net':
lambda *args, **kwargs: neural_predicate(
*args, **kwargs, dataset='test')
},
),
log_iter=1000,
snapshot_iter=len(queries))
def indexATIS():
train_set, valid_set, dicts = load(
'atis.pkl') # load() from data_loader.py
w2idx, la2idx = dicts['words2idx'], dicts['labels2idx']
idx2w = {w2idx[k]: k for k in w2idx}
idx2la = {la2idx[k]: k for k in la2idx}
indexes = {
"idx2w": idx2w,
"idx2la": idx2la,
"w2idx": w2idx,
"la2idx": la2idx
}
with open('embeddings/word_indexes.json', 'w') as f:
json.dump(indexes, f)
log("Word Indexes saved at (embeddings/word_indexes.json)...")
train_x, _, train_label = train_set
valid_x, _, valid_label = valid_set
MAX_LEN = max(max([len(s) for s in train_x]),
max([len(s) for s in valid_x]))
# Add padding
train_x = pad_sequences(train_x,
maxlen=MAX_LEN,
padding='post',
value=w2idx["<UNK>"])
train_label = pad_sequences(train_label,
maxlen=MAX_LEN,
padding='post',
value=la2idx["O"])
valid_x = pad_sequences(valid_x,
maxlen=MAX_LEN,
padding='post',
value=w2idx["<UNK>"])
valid_label = pad_sequences(valid_label,
maxlen=MAX_LEN,
padding='post',
value=la2idx["O"])
train_set = (train_x, train_label) # packing only train_x and train_label
valid_set = (valid_x, valid_label)
return (train_set, valid_set, indexes)
def main():
input_path = os.path.abspath(os.path.join('./data', args.dataset))
dataset = os.path.splitext(args.dataset)[0]
logger.info('Load {}'.format(input_path))
params = {'test_size': 0.2, 'random_state': 1, 'cluster': 'kmeans'}
X_train, X_test, y_train, y_test = data_loader.load(input_path, **params)
logger.info('Split into train and test subsets: {}'.format(params))
params_path = os.path.abspath(os.path.join('./params', args.params))
with open(params_path) as file_:
params = yaml.load(file_, Loader=yaml.SafeLoader)
logger.info('Load {}'.format(params_path))
logger.info('Hyperparameters: {}'.format(params))
models = {
'MLP': nn.MLPClassifier,
'CNN': nn.CNNClassifier,
'RNN': nn.RNNClassifier
}
clf = models[args.model](**params)
estimator = clf.__class__.__name__
logger.info('Train {} on {}'.format(estimator, dataset))
clf.fit(X_train, y_train)
output_dir = os.path.abspath(args.output)
os.makedirs(output_dir, exist_ok=True)
csv_log = pd.DataFrame({
'loss': clf.loss_curve_,
'train_score': clf.training_scores_,
'val_score': clf.validation_scores_
})
csv_log_path = os.path.join(output_dir, time.strftime('%Y%m%d-%H%M%S.csv'))
csv_log.to_csv(csv_log_path)
logger.info('Save learning log to {}'.format(csv_log_path))
if args.plot:
plot_path = os.path.join(output_dir,
time.strftime('%Y%m%d-%H%M%S.png'))
plotting.plot_learning_curve(csv_log_path,
'{} on {}'.format(estimator,
dataset), plot_path)
logger.info('Save learning curves to {}'.format(plot_path))
logger.info('Training score: {}'.format(clf.score(X_train, y_train)))
logger.info('Testing score: {}'.format(clf.score(X_test, y_test)))
logger.info('Done')
def predict(path, output):
print('Prediction ...')
x, y = data_loader.load(path, dim, t)
pipe = load_model(directory + 'model/', m_name)
pred = pipe.predict_proba(x)[:, 0]
new_p = pred.round(2).astype(int)
count = 0
for i in range(len(new_p)):
if new_p[i] == y[i]:
count = count + 1
print('Test result: ',
count,
'/',
len(y),
' (',
round(count / len(y) * 100, 2),
'%)',
sep='')
df = pd.DataFrame({'Expected': y, 'Predicted': new_p})
df.to_csv(output)
def train(model_name, category_type, dump=False):
clf = tfidf_pipeline.make(model_name)
categories = names.categories[category_type]
print 'Loading data...'
data = data_loader.load('full', categories)
train_X, train_y, test_X, test_y = data_loader.split(data, 0.1)
print 'Done.'
print 'Training...'
clf.fit(train_X, train_y)
print 'Done.'
print 'Testing...'
predicted = clf.predict(test_X)
if model_name in ['svr', 'linreg']:
predicted = np.clip(np.round(predicted), 0, 7)
accuracy = scorers.err1(test_y, predicted)
print 'Off-by-one accuracy: ' + str(accuracy)
else:
accuracy = scorers.err0(test_y, predicted)
print 'Exact accuracy: ' + str(accuracy)
print classification_report(test_y, predicted, target_names=categories)
cm = confusion_matrix(test_y, predicted)
print cm
plot.plot_confusion_matrix(cm, category_type)
if dump:
print 'Saving classifier...'
if not exists('dumps'):
makedirs('dumps')
joblib.dump(clf, join('dumps', category_type + '_' + model_name + '_classifier.pkl'))
print 'Done.'
return clf
def main():
df = data_loader.make_df("../input_data/data_h27v07")
bin = 10
rang = np.arange(0, int(1000 / bin) * int(700 / bin))
print(rang)
col_areas = []
for i in rang:
df[f"mean_lai_{i}"] = 0
col_areas.append(f"mean_lai_{i}")
#print(df)
for idx in df.index:
val = data_loader.load(df.loc[idx, "path"])["Lai_500m"]
for i in rang:
j = i // int(1000 / bin)
k = i % int(700 / bin)
df.loc[idx, f"mean_lai_{i}"] = np.mean(
val[j * bin:(j + 1) * bin,
1000 + k * bin:1000 + (k + 1) * bin])
#print(df.loc[idx, :])
print(df.shape)
# df.sort_values("date", ascending=True).plot(x="date", y="mean_lai")
# plt.show()
df.to_csv("../input_data/area_lai_5km_mean.csv")
import matplotlib.pyplot as plt
import numpy as np
import data_loader
from lda import lda_experiment
from logistic import logistic_regression_experiment
from linear import linear_regression_experiment
from qda import qda_experiment
if __name__ == "__main__":
# Train datasets
X_trainA, Y_trainA = data_loader.load("data/trainA")
X_trainB, Y_trainB = data_loader.load("data/trainB")
X_trainC, Y_trainC = data_loader.load("data/trainC")
# Test datasets
X_testA, Y_testA = data_loader.load("data/testA")
X_testB, Y_testB = data_loader.load("data/testB")
X_testC, Y_testC = data_loader.load("data/testC")
# 2.1 : LDA
print("{:=^30}".format("LDA"))
lda_experiment(X_trainA, Y_trainA, X_testA, Y_testA, "A")
lda_experiment(X_trainB, Y_trainB, X_testB, Y_testB, "B")
lda_experiment(X_trainC, Y_trainC, X_testC, Y_testC, "C")
# 2.2 : logistic regression
print("{:=^30}".format("Logistic Regression"))
logistic_regression_experiment(X_trainA, Y_trainA, X_testA, Y_testA, "A")
logistic_regression_experiment(X_trainB, Y_trainB, X_testB, Y_testB, "B")
logistic_regression_experiment(X_trainC, Y_trainC, X_testC, Y_testC, "C")
def dump_results(results, n_clusters):
out_file = f"./output/algs_{n_clusters}clusters.json"
with open(out_file, 'w') as f:
json.dump(results, f, indent=1)
if __name__ == "__main__":
args = parse_arguments()
nr_clusters = args.n_clusters
REPEATS = args.repeats
cluster_metrics = args.metrics
path = os.getcwd()
os.chdir('..')
X = data_loader.load(
"0_data_generators/data_{}_shuffled.csv".format(nr_clusters))
X = np.array(X)
print("Done loading, shape:", X.shape)
os.chdir(path)
raster = Raster(precision=4, threshold=5, min_size=5)
clustering_algorithms = [] if args.no_raster else [('RASTER', raster)]
# 20 for 10 clusters, 300-500 for 100 clusters.
# Don't even try 1000 clusters (a run takes days).
tau = 5 / (X.size) # Clique equivalent of RASTER's threshold
for xsi in args.xsi:
clique = clique_fit.Clique(xsi=xsi, tau=tau)
name = "CLIQUE_xsi" + str(xsi)
clustering_algorithms.append((name, clique))
import json
from train import train_model
from data_loader import load
from examples.NIPS.MNIST.mnist import test_MNIST, MNIST_Net, MNIST_Net2, neural_predicate
from model import Model
from optimizer import Optimizer
from network import Network
import torch
queries = load('train_data.txt')
with open('abs.pl') as f:
problog_string = f.read()
network = MNIST_Net()
network.load_state_dict(torch.load('sd_rec_cnn.pt'))
network.eval()
net = Network(network, 'mnist_net', neural_predicate)
net.optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
model = Model(problog_string, [net], caching=False)
optimizer = Optimizer(model, 2)
log = {}
logs = test_MNIST(model)
for e in logs:
log[e[0]] = e[1]
with open('notl_rec2abs_dpl.json', 'w') as outfile:
json.dump(log, outfile)
def __init__(self,
data_file,
info_file,
xindex=2,
yindex=3,
zindex=4,
default_plot_type=0
):
self.data_file = data_file
self.info_file = info_file
self.xindex = xindex
self.yindex = yindex
self.zindex = zindex
self.plot_limits = default("plot_limits")
self.bin_limits = default("bin_limits")
self.fig = None
self.plot = None
self.options = None
# Load data from files
self.labels, self.data = data_loader.load(info_file, data_file)
# Enumerate available plot types and keep an ordered
# dict mapping descriptions to classes.
# Using an ordered dict means the order in which classes
# are listed in plot_types will be preserved in the GUI.
self.plots = OrderedDict()
for plot_class in plots.plot_types:
self.plots[plot_class.description] = plot_class
#######################################################################
# Combo-box for various plot types
typetitle = gtk.Button("Plot type:")
self.typebox = gtk.combo_box_new_text()
for description in self.plots.keys():
self.typebox.append_text(description)
self.typebox.set_active(default_plot_type) # Set to default plot type
#######################################################################
# Combo box for selecting x-axis variable
xtitle = gtk.Button("x-axis variable:")
self.xbox = gtk.combo_box_new_text()
for label in self.labels.itervalues():
self.xbox.append_text(label)
self.xbox.set_wrap_width(5)
self.xbox.connect('changed', self._cx)
self.xtext = gtk.Entry()
self.xtext.set_text(self.labels[self.xindex])
self.xtext.connect("changed", self._cxtext)
self.xbox.set_active(self.xindex)
#######################################################################
# Combo box for selecting y-axis variable
ytitle = gtk.Button("y-axis variable:")
self.ybox = gtk.combo_box_new_text()
for label in self.labels.itervalues():
self.ybox.append_text(label)
self.ybox.set_wrap_width(5)
self.ybox.connect('changed', self._cy)
self.ytext = gtk.Entry()
self.ytext.set_text(self.labels[self.yindex])
self.ytext.connect("changed", self._cytext)
self.ybox.set_active(self.yindex)
#######################################################################
# Combo box for selecting z-axis variable
ztitle = gtk.Button("z-axis variable:")
self.zbox = gtk.combo_box_new_text()
for label in self.labels.itervalues():
self.zbox.append_text(label)
self.zbox.set_wrap_width(5)
self.zbox.connect('changed', self._cz)
self.ztext = gtk.Entry()
self.ztext.set_text(self.labels[self.zindex])
self.ztext.connect("changed", self._cztext)
self.zbox.set_active(self.zindex)
#######################################################################
# Check buttons for log Scaling
self.logx = gtk.CheckButton('Log x-data.')
self.logy = gtk.CheckButton('Log y-data.')
self.logz = gtk.CheckButton('Log z-data.')
#######################################################################
# Text boxt for plot title
tplottitle = gtk.Button("Plot title:")
self.plottitle = gtk.Entry()
self.plottitle.set_text(default("plot_title"))
#######################################################################
# Legend properties
# Text box for legend title
tlegtitle = gtk.Button("Legend title:")
self.legtitle = gtk.Entry()
self.legtitle.set_text("")
# Combo box for legend position
tlegpos = gtk.Button("Legend position:")
self.legpos = gtk.combo_box_new_text()
for loc in ["best", "upper right", "lower left", "lower right",
"right", "center left", "center right", "lower center",
"upper center", "center", "no legend"]:
self.legpos.append_text(loc)
self.legpos.set_active(0) # Default is first in above list - "best"
#######################################################################
# Spin button for number of bins per dimension
tbins = gtk.Button("Bins per dimension:")
self.bins = gtk.SpinButton()
self.bins.set_increments(10, 10)
self.bins.set_range(5, 10000)
self.bins.set_value(default("nbins"))
#######################################################################
# Axes limits
alimits = gtk.Button("Comma separated plot limits\n"
"x_min, x_max, y_min, y_max:")
self.alimits = gtk.Entry()
self.alimits.connect("changed", self._calimits)
self.alimits.append_text("")
#######################################################################
# Bin limits
blimits = gtk.Button("Comma separated bin limits\n"
"x_min, x_max, y_min, y_max:")
self.blimits = gtk.Entry()
self.blimits.connect("changed", self._cblimits)
self.blimits.append_text("")
#######################################################################
# Check buttons for optional plot elements
self.show_best_fit = gtk.CheckButton("Best-fit")
self.show_posterior_mean = gtk.CheckButton("Posterior mean")
self.show_credible_regions = gtk.CheckButton("Credible regions")
self.show_conf_intervals = gtk.CheckButton("Confidence intervals")
self.show_posterior_pdf = gtk.CheckButton("Posterior PDF")
self.show_prof_like = gtk.CheckButton("Profile Likelihood")
self.show_best_fit.set_active(True)
self.show_posterior_mean.set_active(True)
self.show_credible_regions.set_active(True)
self.show_conf_intervals.set_active(True)
self.show_posterior_pdf.set_active(True)
self.show_prof_like.set_active(True)
#######################################################################
# Make plot button
makeplot = gtk.Button('Make plot.')
makeplot.connect("clicked", self._pmakeplot)
#######################################################################
# Check boxes to control what is saved (note we only attach them to the
# window after showing a plot)
self.save_image = gtk.CheckButton('Save image')
self.save_image.set_active(True)
self.save_summary = gtk.CheckButton('Save statistics in plot')
self.save_summary.set_active(True)
self.save_pickle = gtk.CheckButton('Save pickle of plot')
self.save_pickle.set_active(True)
#######################################################################
# Layout - GTK Table
self.gridbox = gtk.Table(17, 5, False)
self.gridbox.attach(typetitle, 0, 1, 0, 1, xoptions=gtk.FILL)
self.gridbox.attach(self.typebox, 1, 2, 0, 1, xoptions=gtk.FILL)
self.gridbox.attach(xtitle, 0, 1, 1, 2, xoptions=gtk.FILL)
self.gridbox.attach(self.xbox, 1, 2, 1, 2, xoptions=gtk.FILL)
self.gridbox.attach(self.xtext, 1, 2, 2, 3, xoptions=gtk.FILL)
self.gridbox.attach(ytitle, 0, 1, 3, 4, xoptions=gtk.FILL)
self.gridbox.attach(self.ybox, 1, 2, 3, 4, xoptions=gtk.FILL)
self.gridbox.attach(self.ytext, 1, 2, 4, 5, xoptions=gtk.FILL)
self.gridbox.attach(ztitle, 0, 1, 5, 6, xoptions=gtk.FILL)
self.gridbox.attach(self.zbox, 1, 2, 5, 6, xoptions=gtk.FILL)
self.gridbox.attach(self.ztext, 1, 2, 6, 7, xoptions=gtk.FILL)
self.gridbox.attach(self.logx, 0, 1, 2, 3, xoptions=gtk.FILL)
self.gridbox.attach(self.logy, 0, 1, 4, 5, xoptions=gtk.FILL)
self.gridbox.attach(self.logz, 0, 1, 6, 7, xoptions=gtk.FILL)
self.gridbox.attach(tplottitle, 0, 1, 9, 10, xoptions=gtk.FILL)
self.gridbox.attach(self.plottitle, 1, 2, 9, 10, xoptions=gtk.FILL)
self.gridbox.attach(tlegtitle, 0, 1, 10, 11, xoptions=gtk.FILL)
self.gridbox.attach(self.legtitle, 1, 2, 10, 11, xoptions=gtk.FILL)
self.gridbox.attach(tlegpos, 0, 1, 11, 12, xoptions=gtk.FILL)
self.gridbox.attach(self.legpos, 1, 2, 11, 12, xoptions=gtk.FILL)
self.gridbox.attach(tbins, 0, 1, 12, 13, xoptions=gtk.FILL)
self.gridbox.attach(self.bins, 1, 2, 12, 13, xoptions=gtk.FILL)
self.gridbox.attach(alimits, 0, 1, 13, 14, xoptions=gtk.FILL)
self.gridbox.attach(self.alimits, 1, 2, 13, 14, xoptions=gtk.FILL)
self.gridbox.attach(blimits, 0, 1, 14, 15, xoptions=gtk.FILL)
self.gridbox.attach(self.blimits, 1, 2, 14, 15, xoptions=gtk.FILL)
point_plot_container = gtk.VBox()
point_plot_box_upper = gtk.HBox(homogeneous=True)
point_plot_box_lower = gtk.HBox(homogeneous=True)
for check_box in [self.show_conf_intervals,
self.show_credible_regions,
self.show_best_fit]:
point_plot_box_upper.pack_start_defaults(check_box)
for check_box in [self.show_posterior_mean,
self.show_posterior_pdf,
self.show_prof_like]:
point_plot_box_lower.pack_start_defaults(check_box)
point_plot_container.pack_start_defaults(point_plot_box_upper)
point_plot_container.pack_start_defaults(point_plot_box_lower)
self.gridbox.attach(point_plot_container,
0, 2, 15, 16,
xoptions=gtk.FILL)
self.gridbox.attach(makeplot, 0, 2, 16, 17, xoptions=gtk.FILL)
#######################################################################
# Make main GUI window
self.window = gtk.Window()
self.window.maximize()
self.window.set_title("SuperPlot")
# Quit if cross is pressed
self.window.connect('destroy', lambda w: gtk.main_quit())
# Add the table to the window and show
self.window.add(self.gridbox)
self.gridbox.show()
self.window.show_all()
return
import sys
from sklearn.externals.joblib import dump
import data_loader
import names
import tfidf_pipeline
import model_presets
if __name__ == '__main__':
for (category_name, model_name) in [('stars', 'linreg'), ('binary', 'svc')]:
print 'Loading ' + category_name + ' data'
train,_ = data_loader.load('split', names.categories[category_name])
print 'Training ' + model_name
clf = tfidf_pipeline.make(model_name)
clf.fit(train.data, train.target)
print 'Dumping ' + model_name
dump(clf, 'web_clf_' + category_name + '.pkl')
def sgd_optimization_mnist(learning_rate=0.13, n_epochs=1000,
dataset='data/mnist.pkl.gz',
batch_size=600):
training_set, validation_set, testing_set, = data_loader.load(dataset)
training_set_x , training_set_y = training_set
validation_set_x, validation_set_y = validation_set
testing_set_x , testing_set_y = testing_set
# compute number of minibatches for training, validation and testing
n_train_batches = training_set_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = validation_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = testing_set_x.get_value(borrow=True).shape[0] / batch_size
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# allocate symbolic variables for the data
index = tensor.lscalar()
# generate symbolic variables for input (x and y represent a
# minibatch)
x = tensor.matrix('x')
y = tensor.ivector('y')
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
cost = classifier.negative_log_likelihood(y)
# compiling a Theano function that computes the mistakes that are made by
# the model on a minibatch
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: testing_set_x[index * batch_size: (index + 1) * batch_size],
y: testing_set_y[index * batch_size: (index + 1) * batch_size]
}
)
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: validation_set_x[index * batch_size: (index + 1) * batch_size],
y: validation_set_y[index * batch_size: (index + 1) * batch_size]
}
)
# compute the gradient of cost with respect to theta = (W,b)
g_W = tensor.grad(cost=cost, wrt=classifier.W)
g_b = tensor.grad(cost=cost, wrt=classifier.b)
# update the parameters of the model
updates = [(classifier.W, classifier.W - learning_rate * g_W),
(classifier.b, classifier.b - learning_rate * g_b)]
# compiling a Theano function `train_model` that returns the cost, but in
# the same time updates the parameter of the model based on the rules
# defined in `updates`
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: training_set_x[index * batch_size: (index + 1) * batch_size],
y: training_set_y[index * batch_size: (index + 1) * batch_size]
}
)
###############
# TRAIN MODEL #
###############
print '... training the model'
# early-stopping parameters
patience = 5000 # look as this many examples regardless
patience_increase = 2 # wait this much longer when a new best is # found
improvement_threshold = 0.995 # a relative improvement of this much is considered significant
validation_frequency = 5 * n_train_batches # requency of training
best_validation_loss = numpy.inf
test_score = 0.
start_time = timeit.default_timer()
done_looping = False
epoch = 0
while (epoch < n_epochs) and (not done_looping):
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
# iter: number of minibatches used)
iter = epoch * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
# compute zero-one loss on validation set
validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
this_validation_loss = numpy.mean(validation_losses)
print(
'epoch %i, minibatch %i/%i, validation error %f %%' %
(
epoch,
minibatch_index + 1,
n_train_batches,
this_validation_loss * 100.
)
)
# if we got the best validation score until now
if this_validation_loss < best_validation_loss:
#improve patience if loss improvement is good enough
if this_validation_loss < best_validation_loss * improvement_threshold:
patience = max(patience, iter * patience_increase)
# update best_validation_loss
best_validation_loss = this_validation_loss
# test it on the test set
test_losses = [test_model(i) for i in xrange(n_test_batches)]
test_score = numpy.mean(test_losses)
print(
(
' epoch %i, minibatch %i/%i, test error of'
' best model %f %%'
) %
(
epoch,
minibatch_index + 1,
n_train_batches,
test_score * 100.
)
)
# save the best model
with open('best_model.pkl', 'w') as f:
cPickle.dump(classifier, f)
if patience <= iter:
done_looping = True
break
epoch = epoch + 1
end_time = timeit.default_timer()
print(
(
'Optimization complete with best validation score of %f %%,'
'with test performance %f %%'
)
% (best_validation_loss * 100., test_score * 100.)
)
print 'The code run for %d epochs, with %f epochs/sec' % (
epoch, 1. * epoch / (end_time - start_time))
from train import train_model
from model import Model
from network import Network
from standard_networks import FC
from data_loader import load
from optimizer import Optimizer
import torch
train = 2
test = 8
train_queries = load('data/train{}_test{}_train.txt'.format(train, test))
test_queries = load('data/train{}_test{}_test.txt'.format(train, test))
def neural_pred(network, i1, i2):
d = torch.zeros(20)
d[int(i1)] = 1.0
d[int(i2) + 10] = 1.0
d = torch.autograd.Variable(d.unsqueeze(0))
output = network.net(d)
return output.squeeze(0)
fc1 = FC(20, 2)
adam = torch.optim.Adam(fc1.parameters(), lr=1.0)
swap_net = Network(fc1, 'swap_net', neural_pred, optimizer=adam)
#with open('compare.pl') as f:
with open('quicksort.pl') as f:
problog_string = f.read()
import numpy as np
import sklearn.tree
import sklearn.cross_validation
import data_loader
from sklearn.metrics import make_scorer
# Load train data
(X_train, Y_train) = data_loader.load("Dataset/churn.data.txt", standardize=False)
(X_test, Y_test) = data_loader.load("Dataset/churn.test.txt", standardize=False)
def custom_scorer(ground_truth, predictions):
ground_truth = ground_truth
predictions = predictions
prec = sklearn.metrics.precision_score(ground_truth, predictions)
rec = sklearn.metrics.recall_score(ground_truth, predictions)
f1 = sklearn.metrics.f1_score(ground_truth, predictions)
print "prec: " + str(prec)
print "rec: " + str(rec)
print "f1: " + str(f1)
return f1
model = sklearn.tree.DecisionTreeClassifier()
model.fit(X_train, Y_train)
Y_pred = model.predict(X_test)
score = custom_scorer(Y_test, Y_pred)
import numpy as np
from sklearn.svm import *
import sklearn.cross_validation
import data_loader
from sklearn.metrics import make_scorer
from sklearn.feature_selection import SelectFromModel
# Load train data
(X_train, Y_train) = data_loader.load("Dataset/churn.data.txt")
(X_test, Y_test) = data_loader.load("Dataset/churn.test.txt")
def custom_scorer(ground_truth, predictions):
ground_truth = ground_truth
predictions = predictions
prec = sklearn.metrics.precision_score(ground_truth, predictions)
rec = sklearn.metrics.recall_score(ground_truth, predictions)
f1 = sklearn.metrics.f1_score(ground_truth, predictions)
print "prec: " + str(prec)
print "rec: " + str(rec)
print "f1: " + str(f1)
return f1
# Build linear SVM classifier, l1 regularization to perform implicit feature selection
lsvc = LinearSVC(C=0.01, penalty="l1", dual=False).fit(X_train, Y_train)
model = SelectFromModel(lsvc, prefit=True)
features_selected = [elem for selected, elem in zip(model.get_support(), data_loader.get_feature_names()) if selected]
def main():
print("data loading...")
loading = load(data_path)
train_size, test_size, val_size = loading.return_len()
x_train_torch = torch.empty(train_size, 127, 200)
x_val_torch = torch.empty(test_size, 127, 200)
x_test_torch = torch.empty(val_size, 127, 200)
y_train_torch = torch.empty(train_size)
y_val_torch = torch.empty(test_size)
y_test_torch = torch.empty(val_size)
print(x_train_torch.shape)
print(x_test_torch.shape)
print(x_val_torch.shape)
x_train_torch, x_val_torch, x_test_torch, y_train_torch, y_val_torch, y_test_torch = loading.main_processing()
print(x_train_torch.shape, x_val_torch.shape, x_test_torch.shape, y_train_torch.shape, y_val_torch.shape, y_test_torch.shape)
print("data loading success")
x_train_loader = torch.utils.data.DataLoader(x_train_torch, batch_size=batch_size
, shuffle=False, num_workers=0, drop_last=True)
y_train_loader = torch.utils.data.DataLoader(y_train_torch, batch_size=batch_size
, shuffle=False, num_workers=0, drop_last=True)
x_val_loader = torch.utils.data.DataLoader(x_val_torch, batch_size=batch_size
, shuffle=False, num_workers=0, drop_last=True)
y_val_loader = torch.utils.data.DataLoader(y_val_torch, batch_size=batch_size
, shuffle=False, num_workers=0, drop_last=True)
x_test_loader = torch.utils.data.DataLoader(x_test_torch, batch_size=batch_size
, shuffle=False, num_workers=0, drop_last=True)
y_test_loader = torch.utils.data.DataLoader(y_test_torch, batch_size=batch_size
, shuffle=False, num_workers=0, drop_last=True)
model = Text_CNN().to(device) # 모델을 gpu에 올림
loss_func = nn.BCELoss() # lossfunction 정의
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # optimizer 정의
loss_arr = [[0 for i in range(len(x_train_loader))] for j in range(num_epoch)]
train_acc = []
val_acc = []
for i in range(num_epoch):
start = time.time() # 시간 측정
for j, (data, label) in enumerate(zip(x_train_loader, y_train_loader)):
x = data.to(device) # data를 gpu에 올림
y_ = label.to(device) # label을 gpu에 올림
optimizer.zero_grad() # optimizer 초기화
output = model.forward(x) # 모델 foward 진행
loss = loss_func(output, y_) # loss function을 사용해서 loss 측정.
loss.backward() # 가중치에 대한 Loss의 변화량을 측정함.
optimizer.step() # loss가 감소하는 방향으로 가중치를 업데이트
# loss_arr[i].append(loss.cpu().detach().numpy())
loss_arr[i][j] = loss.item()
if j == len(x_train_loader) - 1: # 하나의 epoch를 보면 아래를 실행.
print("Epoch :", i + 1, " Loss :", sum(loss_arr[i], 0.0) / len(loss_arr[i])) # 평균 loss 출력
train_acc.append(eval(x_train_loader, y_train_loader, model))
print("Accuracy of Train Data : {}".format(train_acc[i]))
val_acc.append(eval(x_val_loader, y_val_loader, model))
print("Accuracy of Validation Data : {}".format(val_acc[i]))
loss_arr.append(loss.cpu().detach().numpy())
print("running time :", time.time() - start)
print('---------------------------------------------------------')
# learning rate decay
lr = learning_rate * (0.1 ** (i // learing_rate_decay))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
#print(param_group['lr'])
f1_score(x_val_loader, y_val_loader, model, device)
# Test Data에 대한 Accuracy
print("Accuracy of Test Data : {}".format(eval(x_test_loader, y_test_loader, model)))
# 모델 저장
torch.save(model.state_dict(), './test1.pth')
def run():
data = data_loader.load(DATASET,
n_train=N_TRAIN,
n_test=N_TEST,
train_noise=TRAIN_NOISE,
test_noise=TEST_NOISE,
ood=OOD)
stratify = DATASET not in ["abalone", "segment"]
if DATASET not in [
'arcene', 'moon', 'toy_Story', 'toy_Story_ood', 'segment'
]:
print(DATASET)
x = data_loader.prepare_inputs(data['features'])
y = data['labels']
'''
# check whether the choice of N_OOD is reasonable
classes = np.argmax(y, axis=1)
number_of_each_class = [(classes == ic).sum() for ic in range(int(classes.max()))]
number_of_each_class.reverse()
percentage_of_each_class = np.cumsum(np.array(number_of_each_class)) / np.array(number_of_each_class).sum()
n_ood = np.where(percentage_of_each_class>=0.1)[0][0] + 1
#n_in = y.shape[1] - n_ood
#stratify = classes < n_in
'''
x_train, x_test, y_train, y_test = train_test_split(
x,
y,
train_size=TRAIN_TEST_RATIO,
stratify=y if stratify else None)
else:
#n_ood = int(N_OOD)
if DATASET == 'moon' or DATASET == 'toy_Story' or DATASET == 'toy_Story_ood':
x_train, x_test = data['x_train'], data['x_val']
else:
x_train, x_test = data_loader.prepare_inputs(
data['x_train'], data['x_val'])
y_train, y_test = data['y_train'], data['y_val']
if 'N_OOD' in globals() and N_OOD >= 1:
n_ood = prepare_ood_from_args(data, DATASET, N_OOD)
n_in = y_train.shape[1] - n_ood
# training
train_in_idxs = np.argmax(y_train, axis=1) < n_in
train_ood_idxs = np.argmax(y_train, axis=1) >= n_in
#val_in_idxs = np.argmax(y_val, axis=1) < n_in
#val_ood_idxs = np.argmax(y_val, axis=1) >= n_in
x_train_in = x_train[train_in_idxs]
y_train_in = y_train[train_in_idxs][:, 0:n_in]
x_train_out = x_train[train_ood_idxs]
y_train_out = y_train[train_ood_idxs][:, 0:n_in]
# Generate validation split
x_train_in, x_val_in, y_train_in, y_val_in = train_test_split(
x_train_in,
y_train_in,
train_size=TRAIN_TEST_RATIO,
stratify=y_train_in if stratify else None)
x_val = np.concatenate((x_train_out, x_val_in), axis=0)
y_val = np.concatenate((y_train_out, y_val_in), axis=0)
y_test = y_test[:, 0:n_in]
y_val = y_val[:, 0:n_in]
x_train = x_train_in.astype(np.float32)
x_val = x_val.astype(np.float32)
else:
x_train, x_val, y_train, y_val = train_test_split(
x_train,
y_train,
train_size=TRAIN_TEST_RATIO,
stratify=y_train if stratify else None)
#####################
print('Finish loading data')
gdrive_rpath = './experiments_ood'
t = int(time.time())
log_dir = os.path.join(gdrive_rpath, MODEL_NAME, '{}/logs'.format(t))
if not os.path.exists(log_dir):
os.makedirs(log_dir)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)
file_writer_cm = tf.summary.create_file_writer(log_dir + '/cm')
checkpoint_filepath = os.path.join(gdrive_rpath, MODEL_NAME,
'{}/ckpt/'.format(t))
if not os.path.exists(checkpoint_filepath):
os.makedirs(checkpoint_filepath)
model_path = os.path.join(gdrive_rpath, MODEL_NAME,
'{}/model'.format(format(t)))
if not os.path.exists(model_path):
os.makedirs(model_path)
model_cp_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
save_weights_only=True,
monitor='val_auc_of_ood',
mode='max',
save_best_only=True)
model = build_model(x_train.shape[1], y_train.shape[1], MODEL, args)
def plot_boundary(epoch, logs):
# Use the model to predict the values from the validation dataset.
xy = np.mgrid[-10:10:0.1, -10:10:0.1].reshape(2, -1).T
hat_z = tf.nn.softmax(model(xy, training=False), axis=1)
# scipy.special.softmax(hat_z, axis=1)
c = np.sum(np.arange(hat_z.shape[1] + 1)[1:] * hat_z, axis=1)
# c = np.argmax(np.arange(6)[1:]*scipy.special.softmax(hat_z, axis=1), axis=1
# xy = np.mgrid[-1:1.1:0.01, -2:2.1:0.01].reshape(2,-1).T
figure = plt.figure(figsize=(8, 8))
plt.scatter(xy[:, 0], xy[:, 1], c=c, cmap="brg")
image = plot_to_image(figure)
# Log the confusion matrix as an image summary.
with file_writer_cm.as_default():
tf.summary.image("Boundaries", image, step=epoch)
def plot_boundary_pretrain(epoch, logs):
# Use the model to predict the values from the validation dataset.
xy = np.mgrid[-1:1.1:0.01, -2:2.1:0.01].reshape(2, -1).T
hat_z = tf.nn.softmax(model(xy, training=False), axis=1)
# scipy.special.softmax(hat_z, axis=1)
c = np.sum(np.arange(6)[1:] * hat_z, axis=1)
# c = np.argmax(np.arange(6)[1:]*scipy.special.softmax(hat_z, axis=1), axis=1
# xy = np.mgrid[-1:1.1:0.01, -2:2.1:0.01].reshape(2,-1).T
figure = plt.figure(figsize=(8, 8))
plt.scatter(xy[:, 0], xy[:, 1], c=c, cmap="brg")
image = plot_to_image(figure)
# Log the confusion matrix as an image summary.
with file_writer_cm.as_default():
tf.summary.image("Boundaries_pretrain", image, step=epoch)
border_callback_pretrain = tf.keras.callbacks.LambdaCallback(
on_epoch_end=plot_boundary_pretrain)
border_callback = tf.keras.callbacks.LambdaCallback(
on_epoch_end=plot_boundary)
training_generator = mixup.data_generator(x_train_in,
y_train_in,
batch_size=BATCH_SIZE,
n_channels=N_CHANNELS,
shuffle=SHUFFLE,
mixup_scheme=MIXUP_SCHEME,
k=N_NEIGHBORS,
alpha=ALPHA,
local=LOCAL_RANDOM,
out_of_class=OUT_OF_CLASS,
manifold_mixup=MANIFOLD_MIXUP)
validation_generator = mixup.data_generator(x_val,
y_val,
batch_size=x_val.shape[0],
n_channels=N_CHANNELS,
shuffle=False,
mixup_scheme='none',
alpha=0,
manifold_mixup=MANIFOLD_MIXUP)
test_generator = mixup.data_generator(x_test,
y_test,
batch_size=x_test.shape[0],
n_channels=N_CHANNELS,
shuffle=False,
mixup_scheme='none',
alpha=0,
manifold_mixup=MANIFOLD_MIXUP)
# Pretraining
# if DATASET=='toy_Story':
# pre_x = np.mgrid[-1:1.1:0.01, -2:2.1:0.01].reshape(2,-1).T
# pre_y = .2*np.ones(shape=[pre_x.shape[0], 5])
# model.fit(x=pre_x, y=pre_y, epochs=1, callbacks=[border_callback_pretrain])
training_history = model.fit(
x=training_generator,
validation_data=validation_generator,
epochs=EPOCHS,
callbacks=[
tensorboard_callback,
model_cp_callback,
# border_callback
],
)
print(model.summary())
model.load_weights(checkpoint_filepath)
model.save(model_path)
print('Tensorboard callback directory: {}'.format(log_dir))
metric_file = os.path.join(gdrive_rpath, MODEL_NAME,
'{}/results.txt'.format(t))
loss = model.evaluate(test_generator, return_dict=True)
test_outputs = model.predict(test_generator)
with open(metric_file, "w") as f:
f.write(str(loss))
def run_multiclass(data_dir: str):
data_loader.load(data_dir,
['frame_time_relative', 'dns_qry_name', 'dns_qry_type'])
query_name_learner.run_multiclass()
visualize = 0
torch.manual_seed(2)
#############################################################################
#############################################################################
#############################################################################
############################## Load Data ####################################
#############################################################################
if load_train_data:
from data_loader import load
DATA = load(horizon=21, num_nodes=125, num_layers=3, num_rsc=7)
X_train, Y_train = DATA.read_train(num_sample=10000)
X_train = 1 - X_train
# "X" and "Y" are Nxn matrices where "N" is the number of
# scenarios and "n" is the number of nodes. Each row of
# "X" is a binary vector which has a "0" when the node is
# damaged and "1" when the node is repaired. Each element
# of "Y" gives the time-step at which the node is repair-
# ed and "0" if the node is not damaged.
print("\nTraining data was successfully loaded!\n")
if load_test_data:
num_processes):
## Step 1: Projection (Parallel)
(tiles, scalar) = c_prime.par_map_to_tiles(all_points, precision,
threshold, num_processes)
## Step 2: Agglomeration (Sequential)
clusters = c_prime.raster_clustering_tiles(tiles, min_size)
return (clusters, scalar)
if __name__ == "__main__":
# load input data
data_path = "../0_data_generators/output/data_1000_shuffled.csv"
all_points = dl.load(data_path)
"""
1) RASTER clusters
RASTER projects points to tiles and disregards the former after the
projection has been performed. Thus, it requires merely constant
space, assuming bounded integers or a bounded coordinate system like
the GPS coordinate system for our planet.
Input is projected to points that represent tiles.
"""
precision = 3
threshold = 5
min_size = 4
import data_loader _, test_data = data_loader.load() import mnist_loader training_data, validation_data, _ = mnist_loader.load_data_wrapper() # import network # net = network.Network([784, 30, 10]) # net.learn(training_data, 30, 10, 3.0, test_data) import network2 net = network2.Network([784, 30, 10], cost=network2.CrossEntropyCost) net.large_weight_initializer() net.SGD(training_data, 30, 10, 0.5, evaluation_data=test_data, monitor_evaluation_accuracy=True)
def run():
data = data_loader.load(DATASET,
n_train=N_TRAIN,
n_test=N_TEST,
train_noise=TRAIN_NOISE,
test_noise=TEST_NOISE)
stratify = DATASET not in ["abalone", "segment"]
if DATASET not in [
'arcene', 'moon', 'toy_Story', 'toy_Story_ood', 'segment'
]:
print(DATASET)
x = data_loader.prepare_inputs(data['features'])
y = data['labels']
x_train, x_test, y_train, y_test = train_test_split(
x,
y,
train_size=TRAIN_TEST_RATIO,
stratify=y if stratify else None,
random_state=0)
else:
if DATASET == 'moon' or DATASET == 'toy_Story' or DATASET == 'toy_Story_ood':
x_train, x_test = data['x_train'], data['x_val']
else:
x_train, x_test = data_loader.prepare_inputs(
data['x_train'], data['x_val'])
y_train, y_test = data['y_train'], data['y_val']
# Generate validation split
x_train, x_val, y_train, y_val = train_test_split(
x_train,
y_train,
train_size=TRAIN_TEST_RATIO,
stratify=y_train if stratify else None,
random_state=0)
x_train = x_train.astype(np.float32)
x_val = x_val.astype(np.float32)
x_test = x_test.astype(np.float32)
if 'N_OOD' in globals() and N_OOD >= 1:
n_ood = update_n_ood(data, DATASET, N_OOD)
n_ood = y_val.shape[1] - n_ood - 1
print("Number of ood classes: {n_ood}")
x_train, x_val, x_test, y_train, y_val, y_test, x_ood, y_ood = prepare_ood(
x_train, x_val, x_test, y_train, y_val, y_test, n_ood, NORM)
# x_test_with_ood = np.concatenate([x_test, x_ood], axis=0)
# y_test_with_ood = np.concatenate([y_test, y_ood], axis=0)
x_ood_val, x_ood_test, y_ood_val, y_ood_test = train_test_split(
x_ood, y_ood, test_size=0.5, random_state=0)
x_test_with_ood = np.concatenate([x_test, x_ood_test], axis=0)
y_test_with_ood = np.concatenate([y_test, y_ood_test], axis=0)
x_val_with_ood = np.concatenate([x_val, x_ood_val], axis=0)
y_val_with_ood = np.concatenate([y_val, y_ood_val], axis=0)
else:
n_ood = 0
print('Finish loading data')
gdrive_rpath = './experiments_all'
t = int(time.time())
log_dir = os.path.join(gdrive_rpath, MODEL_NAME, '{}'.format(t))
if not os.path.exists(log_dir):
os.makedirs(log_dir)
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir)
file_writer_cm = tf.summary.create_file_writer(log_dir + '/cm')
checkpoint_filepath = os.path.join(log_dir, 'ckpt')
if not os.path.exists(checkpoint_filepath):
os.makedirs(checkpoint_filepath)
model_path = os.path.join(log_dir, 'model')
if not os.path.exists(model_path):
os.makedirs(model_path)
model_cp_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
save_weights_only=True,
monitor=MONITOR,
mode='max',
save_best_only=True,
verbose=1)
model = build_model(x_train.shape[1], y_train.shape[1], MODEL, args)
def plot_boundary(epoch, logs):
# Use the model to predict the values from the validation dataset.
xy = np.mgrid[-10:10:0.1, -10:10:0.1].reshape(2, -1).T
hat_z = tf.nn.softmax(model(xy, training=False), axis=1)
# scipy.special.softmax(hat_z, axis=1)
c = np.sum(np.arange(hat_z.shape[1] + 1)[1:] * hat_z, axis=1)
# c = np.argmax(np.arange(6)[1:]*scipy.special.softmax(hat_z, axis=1), axis=1
# xy = np.mgrid[-1:1.1:0.01, -2:2.1:0.01].reshape(2,-1).T
figure = plt.figure(figsize=(8, 8))
plt.scatter(xy[:, 0], xy[:, 1], c=c, cmap="brg")
image = plot_to_image(figure)
# Log the confusion matrix as an image summary.
with file_writer_cm.as_default():
tf.summary.image("Boundaries", image, step=epoch)
border_callback = tf.keras.callbacks.LambdaCallback(
on_epoch_end=plot_boundary)
training_generator = mixup.data_generator(x_train,
y_train,
batch_size=BATCH_SIZE,
n_channels=N_CHANNELS,
shuffle=SHUFFLE,
mixup_scheme=MIXUP_SCHEME,
k=N_NEIGHBORS,
alpha=ALPHA,
local=LOCAL_RANDOM,
out_of_class=OUT_OF_CLASS,
manifold_mixup=MANIFOLD_MIXUP)
validation_generator = mixup.data_generator(x_val,
y_val,
batch_size=x_val.shape[0],
n_channels=N_CHANNELS,
shuffle=False,
mixup_scheme='none',
alpha=0,
manifold_mixup=MANIFOLD_MIXUP)
test_generator = mixup.data_generator(x_test,
y_test,
batch_size=x_test.shape[0],
n_channels=N_CHANNELS,
shuffle=True,
mixup_scheme='none',
alpha=0,
manifold_mixup=MANIFOLD_MIXUP)
if N_OOD > 0:
in_out_test_generator = mixup.data_generator(
x_test_with_ood,
y_test_with_ood,
batch_size=x_test_with_ood.shape[0],
n_channels=N_CHANNELS,
shuffle=True,
mixup_scheme='none',
alpha=0,
manifold_mixup=MANIFOLD_MIXUP)
callbacks = [tensorboard_callback, model_cp_callback]
if DATASET == 'Toy_story' or DATASET == 'Toy_story_ood':
border_callback = tf.keras.callbacks.LambdaCallback(
on_epoch_end=cb.plot_boundary)
callbacks += [border_callback]
if MODEL in ['jem', 'jemo', 'jehm', 'jehmo', 'jehmo_mix']:
callbacks += [cb.jem_n_epochs()]
## buffer ##
'''
if MODEL in ['jehmo', 'jehmo_mix']:
if model.with_buffer_out:
model.replay_buffer_out = get_buffer(model.buffer_size,
training_generator.x.shape[1],
x=training_generator.x)
'''
## training ##
t_train_start = int(time.time())
training_history = model.fit(x=training_generator,
validation_data=validation_generator,
epochs=EPOCHS,
callbacks=callbacks)
t_train_end = int(time.time())
used_time = t_train_end - t_train_start
model.load_weights(checkpoint_filepath)
# model.save(model_path)
print('Tensorboard callback directory: {}'.format(log_dir))
ood_loss = 0
metric_file = os.path.join(gdrive_rpath, 'results.txt')
loss = model.evaluate(test_generator, return_dict=True)
# if N_OOD>0:
# ood_loss = model.evaluate(in_out_test_generator, return_dict=True)
# with open(metric_file, "a+") as f:
# f.write(f"{MODEL}, {DATASET}, {t}, {loss['acc_with_ood']:.3f}," \
# f"{loss['ece_metrics']:.3f}, {loss['oe_metrics']:.3f}," \
# f"{loss['loss']:.3f}, {n_ood}, {ood_loss['auc_of_ood']}\n")
if N_OOD > 0:
ood_loss = model.evaluate(in_out_test_generator, return_dict=True)
with open(metric_file, "a+") as f:
f.write(f"{MODEL}, {MIXUP_SCHEME}, {DATASET}, {t}, {loss['accuracy']:.3f}," \
f"{loss['ece_metrics']:.3f}, {loss['oe_metrics']:.3f}," \
f"{ood_loss['accuracy']:.3f}," \
f"{ood_loss['ece_metrics']:.3f}, {ood_loss['oe_metrics']:.3f},"
f"{n_ood}, {ood_loss['auc_of_ood']}, {used_time}\n")
else:
with open(metric_file, "a+") as f:
f.write(f"{MODEL}, {MIXUP_SCHEME}, {DATASET}, {t}, {loss['accuracy']:.3f}," \
f"{loss['ece_metrics']:.3f}, {loss['oe_metrics']:.3f}," \
f"None, " \
f"None, None,"
f"{n_ood}, None, {used_time}\n")
arg_file = os.path.join(log_dir, 'args.txt')
with open(arg_file, "w+") as f:
f.write(str(args))
def __init__(self, grams_file):
self.data = data_loader.load(grams_file)
tk = self.data.keys()[1] if self.data.keys()[0] == '*' else self.data.keys()[0]
self.gram_size = len(tk)
