def _commit(confirmed_, unconfirmed_):
from utils import load_data
from utils import save_data
auditing = load_data(_params['filenames'][5])
confirmed = load_data(_params['filenames'][6])
if not confirmed: confirmed = []
unconfirmed = load_data(_params['filenames'][7])
if not unconfirmed: unconfirmed = []
i = 0
while i < len(auditing):
if auditing[i]['matching'][0]['venue_id'] in confirmed_:
auditing[i]['status'] = 'confirmed'
a = auditing.pop(i)
confirmed.append(a)
i -= 1
elif auditing[i]['matching'][0]['venue_id'] in unconfirmed_:
auditing[i]['status'] = 'unconfirmed'
a = auditing.pop(i)
unconfirmed.append(a)
i -= 1
i += 1
save_data([a[0] for a in auditing], _params['filenames'][5])
save_data(auditing, _params['filenames'][5])
save_data(confirmed, _params['filenames'][6])
save_data(unconfirmed, _params['filenames'][7])
def evaluate(self, goldp = None, silverp = None,
gold_data = None, silver_data = None,
print_score = True):
"""
* Compares two syllabified lists in string format
(e.g. ser-uaes):
gold = ground truth
silver = as predicted by system
* Both lists can be passed as lists (`gold_data`,
`silver_data`) or can be loaded from files
(`goldp`, `silverp`).
* Will return the token-level accuracy and hyphenation
accuracy of the silver predictions (will print these
if `print_score` is True).
"""
if goldp:
gold_data = utils.load_data(goldp)
if silverp:
silver_data = utils.load_data(silverp)
_, gold_Y = self.vectorize(gold_data)
_, silver_Y = self.vectorize(silver_data)
token_acc, hyphen_acc = utils.metrics(utils.pred_to_classes(gold_Y),
utils.pred_to_classes(silver_Y))
if print_score:
print('\t- evaluation scores:')
print('\t\t + token acc:', round(token_acc, 2))
print('\t\t + hyphen acc:', round(hyphen_acc, 2))
return token_acc, hyphen_acc
def run():
# load in members, orient by bioguide ID
print("Loading current legislators...")
current = load_data("legislators-current.yaml")
current_bioguide = { }
for m in current:
if "bioguide" in m["id"]:
current_bioguide[m["id"]["bioguide"]] = m
# remove out-of-office people from current committee membership
print("Sweeping committee membership...")
membership_current = load_data("committee-membership-current.yaml")
for committee_id in list(membership_current.keys()):
for member in membership_current[committee_id]:
if member["bioguide"] not in current_bioguide:
print("\t[%s] Ding ding ding! (%s)" % (member["bioguide"], member["name"]))
membership_current[committee_id].remove(member)
save_data(membership_current, "committee-membership-current.yaml")
# remove out-of-office people from social media info
print("Sweeping social media accounts...")
socialmedia_current = load_data("legislators-social-media.yaml")
for member in list(socialmedia_current):
if member["id"]["bioguide"] not in current_bioguide:
print("\t[%s] Ding ding ding! (%s)" % (member["id"]["bioguide"], member["social"]))
socialmedia_current.remove(member)
save_data(socialmedia_current, "legislators-social-media.yaml")
def main(state_num):
matches_filename = 'matches_%d' % state_num
print 'Loading %s ...' % matches_filename
matches = utils.load_data(matches_filename)
matches_reduced_filename = 'matches_reduced'
try:
print "Loading matches_reduced ..."
matches_reduced = utils.load_data(matches_reduced_filename)
except:
print "Matches_reduced doesn't exists, creating new."
matches_reduced = {}
num_matches = len(matches.keys())
for keyIdx, matchId in enumerate(matches.keys()):
print "\rMatch %d out of %d [%0.1f%%]" % (keyIdx + 1, num_matches, (keyIdx + 1) / float(num_matches) * 100),
summoners = []
num_summoners = len(matches[matchId]['participants'])
for i in range(num_summoners):
champLevel = matches[matchId]['participants'][i]['stats']['champLevel']
summonerId = matches[matchId]['participantIdentities'][i]['player']['summonerId']
winner = matches[matchId]['participants'][i]['stats']['winner']
summoners += [{'champLevel': champLevel, 'summonerId': summonerId, 'winner': winner}]
matches_reduced[matchId] = {'summoners': summoners}
print "Saving %s ..." % matches_reduced_filename
utils.save_data(matches_reduced, matches_reduced_filename)
print "Done!"
def main():
parser = argparse.ArgumentParser(description='Generate input files for hunalign')
parser.add_argument('ja', help='tokenized ja json')
parser.add_argument('en', help='tokenized en json')
parser.add_argument('prefix', help='output prefix')
parser.add_argument('batchfile', help='output batchfile')
parser.add_argument('--b', help='approximate batch size', type=int, default=5000)
args = parser.parse_args()
recipes_ja = utils.load_data(args.ja)
recipes_en = utils.load_data(args.en)
iteration = 1
langs = ('ja', 'en')
num_lines = [0 for _ in langs] # keep track of the number of lines printed out
output_filenames = [get_filename(args.prefix, iteration, lang) for lang in langs]
output_files = [open(filename, 'w') for filename in output_filenames]
batchfile_output = [(output_filenames[0], output_filenames[1],
get_filename(args.prefix, iteration, 'align'))]
for recipes in izip(recipes_ja, recipes_en):
for index, lang in enumerate(langs):
recipe = recipes[index]
output_file = output_files[index]
print_item_to_file(output_file, recipe['name'])
print_items_to_file(output_file, recipe['description'])
print_itemss_to_file(output_file, recipe['instructions'])
print_items_to_file(output_file, recipe['advice'])
print_items_to_file(output_file, recipe['history'])
num_lines[index] += (1 + # name
len(recipe['description']) +
sum(map(lambda inst: len(inst), recipe['instructions'])) +
len(recipe['advice']) +
len(recipe['history']))
if any(map(lambda num_line: num_line > args.b, num_lines)):
for output_file in output_files:
output_file.close()
# reset
iteration += 1
num_lines = [0 for _ in langs]
output_filenames = [get_filename(args.prefix, iteration, lang) for lang in langs]
output_files = [open(filename, 'w') for filename in output_filenames]
batchfile_output.append((output_filenames[0], output_filenames[1],
get_filename(args.prefix, iteration, 'align')))
for output_file in output_files:
output_file.close()
with open(args.batchfile, 'w') as f:
for output in batchfile_output:
f.write('{0}\t{1}\t{2}\n'.format(*output))
def main():
train_url = "e:/data/comment_sentiment/train_set.csv"
test_url = "e:/data/comment_sentiment/test_set.csv"
x, y = utils.load_data(train_url)
test_x, test_y = utils.load_data(test_url)
lr = LogisticRegression()
lr.train_model(x, y)
pre_y = lr.predict(test_x)
utils.show_result(test_y, pre_y, "logistic_comment")
def main():
train_url = "train_set.csv"
test_url = "test_set.csv"
x, y = utils.load_data(train_url)
test_x, test_y = utils.load_data(test_url)
gda = GDA()
gda.train_model(x, y)
pre_y = gda.predict(test_x)
utils.show_result(test_y, pre_y, "gda_comment")
def main():
train_url = "e:/data/comment_sentiment/train_set.csv"
test_url = "e:/data/comment_sentiment/test_set.csv"
train_x, train_y = utils.load_data(train_url)
test_x, test_y = utils.load_data(test_url)
bayes = Bayes()
bayes.train_model(train_x, train_y)
pre_y = bayes.predict(test_x)
utils.show_result(test_y, np.array([pre_y]).T, "bayes_comment")
def process_categories():
reviews = load_data("vegas_reviews.json")
def pizza(line):
return 'Pizza' in line['categories']
vegas_cats = set(food_lib.map_to_arg(load_data("food_businesses.json", pizza), 'business_id'))
times = []
for r in reviews:
if r['business_id'] in vegas_cats:
times.append(r['date'])
print(sorted(times))
def _load_dbs():
global user_dict
global movie_dict
try:
movie_dict = load_data(movie_pkl_file_name)
except IOError:
print 'there is no pkl file named %s' % movie_pkl_file_name
try:
user_dict = load_data(user_pkl_file_name)
except IOError:
print 'there is no pkl file named %s' % user_pkl_file_name
def create_sorted_dict(self, offline=False):
if offline:
# Offline c*k zaman aliyor, gerek yok gibi bir sey.
#TODO Try-except koymak lazim, dosyalar yok belki
self.cf_simsorted_dict = load_data(PKL + 'cf_simsorted.pkl')
self.cb_simsorted_dict = load_data(PKL + 'cb_simsorted.pkl')
else:
if self.cb_prox is None or self.cf_prox is None:
self.create_proximity_matrices()
self.cb_simsorted_dict = utils.sortSparseMatrix(self.cb_prox)
print "cb dict has been calculated"
self.cf_simsorted_dict = utils.sortSparseMatrix(self.cf_prox)
def main():
parser = argparse.ArgumentParser(description='Check whether ja-en recipes '
'have same number of ingredients')
parser.add_argument('ja', help='sorted ja recipes')
parser.add_argument('en', help='sorted en recipes')
args = parser.parse_args()
recipes_ja = utils.load_data(args.ja)
recipes_en = utils.load_data(args.en)
for recipe_ja, recipe_en in izip(recipes_ja, recipes_en):
assert(recipe_ja['id'] == recipe_en['id'])
if len(recipe_ja['ingredients']) != len(recipe_en['ingredients']):
print(recipe_ja['id'])
def main(args):
'''Module main function'''
global database
global genetic_algorithm
global joint_positions
global goal_positions
pygame.init()
random.seed()
database = utils.initialize_database(args, 'RobotTrainingData')
database.set_objective_names(['Tiempo', r'Error en $\theta_1$', r'Error en $\theta_2$', r'Error en $\theta_3$', 'Energía'])
problem = EV3Problem()
generation = database.properties['highest_population']
population_size = database.properties['population_size']
genetic_algorithm = evolution.NSGA(problem, population_size)
x_path = os.path.abspath(pkg_resources.resource_filename('resources.ev3', 'x_train.txt'))
y_path = os.path.abspath(pkg_resources.resource_filename('resources.ev3', 'y_train.txt'))
batch_start = (generation % 10) * N_GOALS
joint_positions = np.loadtxt(x_path)[batch_start : batch_start + N_GOALS, :]
goal_positions = np.loadtxt(y_path)[batch_start : batch_start + N_GOALS, :]
if generation > 0:
parents, children = utils.load_data(database)
genetic_algorithm.set_population(parents)
genetic_algorithm.set_children(children)
for _ in range(args.iterations):
generation += 1
print('Starting generation ' + str(generation))
genetic_algorithm.iterate()
database.create_population()
utils.save_data(genetic_algorithm, database)
print('=' * (SCREEN_WIDTH - 1))
def compute_pval_rsa(seed):
stim, voxels = load_data(n_samples, n_features, model=model, seed=seed,
heteroscedastic=heteroscedastic)
# compute similarity
stim_ = stim
if stim.shape[1] == 1:
stim_ = np.hstack((stim, - stim))
stim_similarity = square_pdist(stim_) # np.corrcoef(stim_)
voxels_similarity = square_pdist(voxels) # np.corrcoef(voxels)
# indices to extract lower triangular part of a matrix
lw_idx = np.triu_indices(n_samples, k=1)
stim_vsim = stim_similarity[lw_idx]
voxels_vsim = voxels_similarity[lw_idx]
# compute the statistic
# T = np.corrcoef(stim_vsim, voxels_vsim)[0, 1]
T = spearmanr(voxels_vsim, stim_vsim)[0]
T_perm = []
for i in range(n_draws):
# permute the labels
perm = np.random.permutation(n_samples)
# voxels_vsim_perm = np.corrcoef(voxels[perm])[lw_idx]
voxels_vsim_perm = square_pdist(voxels[perm])[lw_idx]
# compute the test statistic
# T_perm.append(np.corrcoef(voxels_vsim_perm, stim_vsim)[0, 1])
T_perm.append(spearmanr(voxels_vsim_perm, stim_vsim)[0])
pval = 1 - percentileofscore(np.array(T_perm), T) / 100.
return pval
def t1_3():
data = utils.load_data('data2D.npy').astype("float32")
for k in [3]:
rvals = kmeans(data, 1e-3, k, epochs=1000)
t_loss = rvals['training_loss']
v_loss = rvals['validation_loss']
mu = rvals['mu']
plt.clf()
fig = plt.figure(1, figsize=(16,12))
plt.plot(np.arange(len(t_loss)), t_loss)
plt.savefig("t12_2_k%d.png" % k)
t = classify(data, mu)
colors = iter(cm.rainbow(np.linspace(0, 1, len(t))))
plt.clf()
#fig = plt.figure(1, figsize=(16,12))
for i in range(len(t)):
print 'plotting scatter...'
print 'cluster x, y shape ', t[i][:, 0].shape, t[i][:, 1].shape
print 'cluster x, y shape ', t[i][:, 0].shape, t[i][:, 1].shape
s = plt.scatter(t[i][:, 0], t[i][:, 1], color=next(colors))
#print "returned ", s
plt.show()
plt.savefig('t12_3_scatter_k%d.png' % (i))
def main():
if len(sys.argv) != 2:
print 'Usage:\t./next_day_prediction.py TICKER_SYMBOL'
print 'Ex:\t./next_day_prediction.py NFLX'
sys.exit()
k = 10
D = utils.load_data('i30/stocks/' + sys.argv[1] + '.csv')
#load in past year's data
train = D[-365:]
X_train, y_train = utils.timestep_transform(train, k)
model, scaler = utils.generate_model(X_train, y_train)
pred_val = model.predict(scaler.transform([utils.day_transform(D, k)])).tolist()
print 'Current day closing value:'
print '\t', D[-1][-2]
print 'Projected change in closing value:'
print '\t', 100*(pred_val[0]-1)
print 'Project next day closing value:'
print '\t', pred_val[0]*D[-1][-2]
def _load_statistics():
global _statistics
filename = get_path('datasets' , 'statistics.json')
_statistics = load_data(filename, verbose=False)
if not _statistics or not isinstance(_statistics, dict) :
_statistics = {}
print ('')
def main():
parser = argparse.ArgumentParser(description='Tokenize all')
parser.add_argument('recipes', help='recipes.json')
parser.add_argument('--lang', choices=('en', 'ja'))
args = parser.parse_args()
recipes = utils.load_data(args.recipes)
for recipe in recipes:
name = word_tokenize(recipe['name'], args.lang)
description = sent_word_tokenize(recipe['description'], args.lang)
ingredients_name = map(lambda ing_name: word_tokenize(ing_name, args.lang),
map(lambda ing: ing['name'], recipe['ingredients']))
ingredients_quantity = map(lambda ing_qt: word_tokenize(ing_qt, args.lang),
map(lambda ing: ing['quantity'], recipe['ingredients']))
ingredients = map(lambda pair: {'name': pair[0], 'quantity': pair[1]},
zip(ingredients_name, ingredients_quantity))
instructions = map(lambda inst: sent_word_tokenize(inst, args.lang),
recipe['instructions'])
advice = sent_word_tokenize(recipe['advice'], args.lang)
history = sent_word_tokenize(recipe['history'], args.lang)
recipe = {
'id': recipe['id'],
'name': name,
'description': description,
'ingredients': ingredients,
'instructions': instructions,
'advice': advice,
'history': history,
}
print(json.dumps(recipe))
def next_day_prediction(ticker_symbol, training_days):
k = 10
D = utils.load_data('i30/stocks/' + ticker_symbol + '.csv')
#load in past year's data
train = D[-1*training_days:]
X_train, y_train = utils.timestep_transform(train, k)
model, scaler = utils.generate_model(X_train, y_train)
pred_val = model.predict(scaler.transform([utils.day_transform(D, k)])).tolist()
curr_close = D[-1][-2]
change = pred_val[0]-1
next_close = pred_val[0]*curr_close
'''
print 'Current day closing value:'
print '\t', curr_close
print 'Projected change in closing value:'
print '\t', 100*change
print 'Project next day closing value:'
print '\t', next_close
'''
return (curr_close, change, next_close)
def train(in_file):
xvals, yvals = utils.load_data(in_file)
xvals, yvals = utils.randomize(xvals, yvals)
network = build_network()
model = tflearn.DNN(network)
model.fit(xvals, yvals, n_epoch=200, validation_set=0.2)
model.save('circle.tflearn')
def run():
options = utils.flags()
debug = options.get('debug', False)
filename = "legislators-current.yaml"
args = utils.args()
legislators = load_data(filename)
if len(args) != 0:
bioguides = args
print("Fetching contact forms for %s..." % ', '.join(bioguides))
else:
bioguides = [member['id']['bioguide'] for member in legislators]
print("Fetching contact forms for all current members...")
for legislator in legislators:
bioguide = legislator['id']['bioguide']
if bioguide not in bioguides: continue
if bioguide in SKIP_BIOGUIDES: continue
if debug: print("Downloading form for %s" % bioguide, flush=True)
try:
steps = contact_steps_for(bioguide)
except LegislatorNotFoundError as e:
if debug: print("skipping, %s..." % e, flush=True)
continue
legislator['terms'][-1]['contact_form'] = steps['contact_form']['steps'][0]['visit']
print("Saving data to %s..." % filename)
save_data(legislators, filename)
def main(args):
path = utils.get_data_path(args.site[0])
urls = utils.load_urls(path)
for count in range(2, len(urls) + 1):
print '[learner] clustering with %d urls' % count
# load data
data = [utils.load_data(path, id) for id, url in enumerate(urls)]
data = data[:count]
# process data
processor = processors.Processor(data)
features = processor.extract()
# clustering
clusterer = clusterers.DBSCAN()
labels = clusterer.cluster(features).labels_
# score
clusters = processor.score(labels)
with open(os.path.join(path, 'clusters.%03d.json' % count), 'w') as f:
f.write(json.dumps(clusters, indent=2, ensure_ascii=False).encode('utf8'))
def stacker_data_v2(cutoff, num_least_correlated_cols):
X, y_train = load_data()
oof_predictions, lb_predictions, oof_ginis = load_predictions_with_cutoff(PREDICTION_PATH, cutoff)
X_train, X_test = oof_predictions, lb_predictions
new_cols = least_correlated_cols(X_train, num_least_correlated_cols)
X_train, X_test = X_train[new_cols], X_test[new_cols]
return X_train, y_train, X_test
def data_v1():
X, y_train = load_data()
X = pd.get_dummies(X)
is_train_obs = X.index.get_level_values('obs_type') == 'train'
X_train, X_test = X[is_train_obs], X[~is_train_obs]
return X_train, y_train, X_test
def test_cA(learning_rate=0.01, training_epochs=20,
dataset='../datasets/mnist.pkl.gz',
batch_size=10, output_folder='cA_plots', contraction_level=.1):
datasets = load_data(dataset)
train_set_x, train_set_y = datasets[0]
n_train_batches = train_set_x.get_value(borrow=True).shape[0]
n_train_batches /= batch_size
index = T.lscalar()
x = T.matrix('x')
if not os.path.isdir(output_folder):
os.makedirs(output_folder)
os.chdir(output_folder)
rng = numpy.random.RandomState(123)
ca = cA(numpy_rng=rng, input=x,
n_visible=28 * 28, n_hidden=500, n_batchsize=batch_size)
cost, updates = ca.get_cost_updates(contraction_level=contraction_level,
learning_rate=learning_rate)
train_ca = theano.function(
[index],
[T.mean(ca.L_rec), ca.L_jacob],
updates=updates,
givens={
x: train_set_x[index * batch_size: (index + 1) * batch_size]
}
)
start_time = timeit.default_timer()
for epoch in xrange(training_epochs):
c = []
for batch_index in xrange(n_train_batches):
c.append(train_ca(batch_index))
c_array = numpy.vstack(c)
print 'Training epoch %d, reconstruction cost ' % epoch, numpy.mean(
c_array[0]), ' jacobian norm ', numpy.mean(numpy.sqrt(c_array[1]))
end_time = timeit.default_timer()
training_time = (end_time - start_time)
print >> sys.stderr, ('The code for file ' + os.path.split(__file__)[1] +
' ran for %.2fm' % ((training_time) / 60.))
image = Image.fromarray(tile_raster_images(
X=ca.W.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(10, 10),
tile_spacing=(1, 1)))
image.save('cae_filters.png')
os.chdir('../')
def load_data(random_state=1066, n=1000, max_phrase_length=100):
data = utils.load_data(random_state=random_state,
n=n,
max_phrase_length=max_phrase_length)
X_train, y_train = data[0]
X_valid, y_valid = data[1]
X_test, y_test = data[2]
X_train = X_train.reshape((-1, max_phrase_length, 67)).transpose(0, 2, 1)
X_valid = X_valid.reshape((-1, max_phrase_length, 67)).transpose(0, 2, 1)
X_test = X_test.reshape((-1, max_phrase_length, 67)).transpose(0, 2, 1)
# Robert: what about reshaping this data for 1D convs?
# hstack() instead of hstack() in when creatign X in utils?
return dict(
X_train=theano.shared(lasagne.utils.floatX(X_train)),
y_train=T.cast(theano.shared(y_train), 'int32'),
X_valid=theano.shared(lasagne.utils.floatX(X_valid)),
y_valid=T.cast(theano.shared(y_valid), 'int32'),
X_test=theano.shared(lasagne.utils.floatX(X_test)),
y_test=T.cast(theano.shared(y_test), 'int32'),
num_examples_train=X_train.shape[0],
num_examples_valid=X_valid.shape[0],
num_examples_test=X_test.shape[0],
#input_height=X_train.shape[2], # what's the equivalent in our vectors?
#input_width=X_train.shape[3],
output_dim=5, # since five sentiment class
)
def _match_from_models(model_filename, search_func, get_entity_by_id_func, filenames, threshold, prompt, verbose=True):
from utils import load_data
if verbose == True: print ('Loading models...')
models = load_data(model_filename)
if verbose == True: print ('Done.')
models = _pre_process_models(models, filenames)
_run_match(models, search_func, get_entity_by_id_func, filenames, threshold, prompt)
def data_v5():
X, y_train = load_data()
X = pd.get_dummies(X)
X.drop(['T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], axis=1, inplace=True)
is_train_obs = X.index.get_level_values('obs_type') == 'train'
X_train, X_test = X[is_train_obs], X[~is_train_obs]
return X_train, y_train, X_test
def load_triangle():
"""
Get the text into a 2D array of ints.
"""
triangle_string = utils.load_data(FILENAME)
triangle_lines = [line for line in triangle_string.split('\n')]
triangle = [[int(x) for x in line.split()] for line in triangle_lines]
return triangle
def load_higgs_data(data_file, valid_size, normalize):
# we get back a tuple of train data, test data, train weights, train labels, and test labels
dataset = load_data(data_file, valid_size, encoding='integer', normalize=normalize)
train_set_x, train_set_y = load_shared_dataset((dataset[0], dataset[3]))
valid_set_x, valid_set_y = load_shared_dataset((dataset[1], dataset[4]))
return [(train_set_x, train_set_y), (valid_set_x, valid_set_y)]
import project1 as p1
import utils
import numpy as np
#-------------------------------------------------------------------------------
# Data loading. There is no need to edit code in this section.
#-------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
val_data = utils.load_data('reviews_val.tsv')
test_data = utils.load_data('reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment']) for sample in train_data))
val_texts, val_labels = zip(*((sample['text'], sample['sentiment']) for sample in val_data))
test_texts, test_labels = zip(*((sample['text'], sample['sentiment']) for sample in test_data))
dictionary = p1.bag_of_words(train_texts)
train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
#-------------------------------------------------------------------------------
# Problem 5
#-------------------------------------------------------------------------------
toy_features, toy_labels = toy_data = utils.load_toy_data('toy_data.tsv')
T = 10
L = 0.2
batch_size = 128
epochs = 10
img_size = 224 # input image dimensions
channel_size = 1
label_size = 1 # label dimensions
img_dims = (img_size, img_size, channel_size)
label_dims = (label_size, label_size)
filepath_labels = 'lol_labels.txt'
filepath_data = 'lol_images.zip'
data_size = 60000
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = load_data()
x_train = x_train.reshape(x_train.shape[0], img_size, img_size, channel_size)
x_test = x_test.reshape(x_test.shape[0], img_size, img_size, channel_size)
input_shape = (img_size, img_size, channel_size)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
###### Model 0 ##############################
model = Sequential()
model.add(
def load_normalize_data(path):
data, label = load_data(path)
processed_data = data_preprocessing(data)
return processed_data, label
y_batch = y_batch.to(device=args.device)
out = net(P, roots, X_batch, X_batch_daily, X_batch_weekly, r2sDic,
s2rDic, randomtrajs, mask1)
loss = masked_mae_loss(out, y_batch)
loss.backward()
optimizer.step()
epoch_training_losses.append(loss.detach().cpu().numpy())
return sum(epoch_training_losses) / len(epoch_training_losses)
if __name__ == '__main__':
torch.manual_seed(1)
X, r2sDic, s2rDic, trajDic, keys = load_data(pathNum, pathLen)
split_line1 = int(X.shape[2] * 0.7)
split_line2 = int(X.shape[2] * 0.8)
split_line3 = int(X.shape[2])
np.save("train_cd.npy", X[:, :, :split_line1])
np.save("val_cd.npy", X[:, :, split_line1:split_line2])
np.save("test_cd.npy", X[:, :, split_line2:])
means = np.mean(X[:, :, :split_line1], axis=(0, 2))
stds = np.std(X[:, :, :split_line1], axis=(0, 2))
X = X - means[0]
X = X / stds[0]
print(means)
print(stds)
print(X.shape)
else:
return False
parser = argparse.ArgumentParser()
parser.add_argument('--lr', type=float, default=1e-2, help='Learning rate for the parameters')
parser.add_argument('--wd', type=float, default=1e-2, help='Weight decay for the parameters')
parser.add_argument('--n_hid', type=int, default=112, help='hidden layer for RNN')
parser.add_argument('--n_iter', type=int, default=9, help='(time-steps + 1) for RNN')
parser.add_argument('--dataset', type=str, default='cora', help='dataset, also use "citeseer" or "pubmed"')
parser.add_argument('--ps', type=int, default=5, help='patience for early stopping')
parser.add_argument('--d1', type=float, default=0.2, help='dropout rate for RNN')
parser.add_argument('--d2', type=float, default=0.2, help='dropout rate for dense(attention)')
parser.add_argument('--d3', type=float, default=0.4, help='dropout rate for dense(classification)')
arg = parser.parse_args()
features_, labels_, adj, deg, deg_inv = load_data(arg.dataset)
P = torch.from_numpy(deg_inv.dot(adj.todense()))
features = torch.from_numpy(features_.todense())
labels = torch.from_numpy(labels_).long()
n_nodes, n_feats = features_.shape[0], features_.shape[1]
n_class = np.int(np.max(labels_) + 1)
### Belows are the hyperparameters
n_hids = arg.n_hid
n_iters = arg.n_iter
d1 = arg.d1 # Dropout rate for RNN
d2 = arg.d2 # Dropout rate for attention
d3 = arg.d3 # Dropout rate for dense(classification)
n_epochs = arg.n_iter
lr = arg.lr # Learning rate for the parameters
wd = arg.wd # Weight decay for the parameters
ps = arg.ps #Patience rate for Early Stopping
import utils n_players, max_marble = utils.load_data() print(utils.compute_max_score(n_players, max_marble))
def train(model, supervisor, num_label):
trX, trY, num_tr_batch, valX, valY, num_val_batch = load_data(
cfg.dataset, cfg.batch_size, is_training=True)
Y = valY[:num_val_batch * cfg.batch_size].reshape((-1, 1))
fd_train_acc, fd_loss, fd_val_acc = save_to()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with supervisor.managed_session(config=config) as sess:
print("\nNote: all of results will be saved to directory: " +
cfg.results)
for epoch in range(cfg.epoch):
print("Training for epoch %d/%d:" % (epoch, cfg.epoch))
if supervisor.should_stop():
print('supervisor stoped!')
break
for step in tqdm(range(num_tr_batch),
total=num_tr_batch,
ncols=70,
leave=False,
unit='b'):
start = step * cfg.batch_size
end = start + cfg.batch_size
global_step = epoch * num_tr_batch + step
if global_step % cfg.train_sum_freq == 0:
_, loss, train_acc, summary_str = sess.run([
model.train_op, model.total_loss, model.accuracy,
model.train_summary
])
assert not np.isnan(
loss), 'Something wrong! loss is nan...'
supervisor.summary_writer.add_summary(
summary_str, global_step)
fd_loss.write(str(global_step) + ',' + str(loss) + "\n")
fd_loss.flush()
fd_train_acc.write(
str(global_step) + ',' +
str(train_acc / cfg.batch_size) + "\n")
fd_train_acc.flush()
else:
sess.run(model.train_op)
if cfg.val_sum_freq != 0 and (
global_step) % cfg.val_sum_freq == 0:
val_acc = 0
for i in range(num_val_batch):
start = i * cfg.batch_size
end = start + cfg.batch_size
acc = sess.run(
model.accuracy, {
model.X: valX[start:end],
model.labels: valY[start:end]
})
val_acc += acc
val_acc = val_acc / (cfg.batch_size * num_val_batch)
fd_val_acc.write(
str(global_step) + ',' + str(val_acc) + '\n')
fd_val_acc.flush()
if (epoch + 1) % cfg.save_freq == 0:
supervisor.saver.save(
sess, cfg.logdir + '/model_epoch_%04d_step_%02d' %
(epoch, global_step))
fd_val_acc.close()
fd_train_acc.close()
fd_loss.close()
model = DQN(state_dim, NUM_ACTIONS, NUM_OBJECTS)
optimizer = optim.SGD(model.parameters(), lr=ALPHA)
single_run_epoch_rewards_test = []
pbar = tqdm(range(NUM_EPOCHS), ncols=80)
for _ in pbar:
single_run_epoch_rewards_test.append(run_epoch())
pbar.set_description(
"Avg reward: {:0.6f} | Ewma reward: {:0.6f}".format(
np.mean(single_run_epoch_rewards_test),
utils.ewma(single_run_epoch_rewards_test)))
return single_run_epoch_rewards_test
if __name__ == '__main__':
state_texts = utils.load_data('game.tsv')
dictionary = utils.bag_of_words(state_texts)
state_dim = len(dictionary)
# set up the game
framework.load_game_data()
epoch_rewards_test = [] # shape NUM_RUNS * NUM_EPOCHS
for _ in range(NUM_RUNS):
epoch_rewards_test.append(run())
epoch_rewards_test = np.array(epoch_rewards_test)
x = np.arange(NUM_EPOCHS)
fig, axis = plt.subplots()
def train(self, epochs, batch_size=32, sample_interval=500, start_point=0):
# Load the dataset
X_train, y_train = utils.load_data(self.writer)
# Adversarial ground truths
valid = np.ones((batch_size, 1))
fake = np.zeros((batch_size, 1))
for epoch in range(start_point, epochs):
# ---------------------
# Train Discriminator
# ---------------------
# Select a random batch of images
idx = np.random.randint(0, X_train.shape[0], batch_size)
imgs = X_train[idx]
# Sample noise as generator input
noise = np.random.normal(0, 1, (batch_size, self.latent_dim))
# The labels of the digits that the generator tries to create an
# image representation of
sampled_labels = np.random.uniform(0, 1,
(batch_size, self.num_classes))
sampled_labels = np.around(sampled_labels)
# Generate a half batch of new images
gen_imgs = self.generator.predict([noise, sampled_labels])
# Image labels. 0-9
img_labels = y_train[idx]
# Train the discriminator
d_loss_real = self.discriminator.train_on_batch(
imgs, [valid, img_labels])
d_loss_fake = self.discriminator.train_on_batch(
gen_imgs, [fake, sampled_labels])
d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)
# ---------------------
# Train Generator
# ---------------------
# Train the generator
g_loss = self.combined.train_on_batch([noise, sampled_labels],
[valid, sampled_labels])
# Plot the progress
print(
"%d [D loss: %f, acc.: %.2f%%, op_acc: %.2f%%] [G loss: %f]" %
(epoch, d_loss[0], 100 * d_loss[3], 100 * d_loss[4],
g_loss[0]))
utils.write_log(
self.writer,
['D loss', 'G loss', 'accuracy', 'class accuracy'],
[d_loss[0], g_loss[0], 100 * d_loss[3], 100 * d_loss[4]],
epoch)
# If at save interval => save generated image samples
if epoch % sample_interval == 0:
utils.save_model(
'%s/' %
('acgan' if self.flags.name is None else self.flags.name),
self.generator, self.discriminator, epoch)
self.sample_images(epoch)
def main(filename):
"""
Main function for generating submissions.
"""
y_pred_all = []
X_train, y_train_all, X_test = load_data()
for n in range(3):
print(
"############## working on dataset {} ###################".format(
str(n + 1)))
# process
y_train = 2 * np.array(y_train_all[2000 * n:2000 * (n + 1)]) - 1
k, n_mismatch = 13, 3
if n != 0:
print("Compute gram matrix for first kernel")
gram_train_13_3, gram_test_13_3 = get_gram_matrix(
X_train[2000 * n:2000 * (n + 1)],
X_test[1000 * n:1000 * (n + 1)],
k=k,
n_mismatch=n_mismatch,
n_kernel=n + 1,
)
k, n_mismatch = 12, 2
if n != 0:
print("Compute gram matrix for second kernel ")
gram_train_12_2, gram_test_12_2 = get_gram_matrix(
X_train[2000 * n:2000 * (n + 1)],
X_test[1000 * n:1000 * (n + 1)],
k=k,
n_mismatch=n_mismatch,
n_kernel=n + 1,
)
print("Compute gram matrix for third kernel ")
k, n_mismatch = 13, 2
gram_train_13_2, gram_test_13_2 = get_gram_matrix(
X_train[2000 * n:2000 * (n + 1)],
X_test[1000 * n:1000 * (n + 1)],
k=k,
n_mismatch=n_mismatch,
n_kernel=n + 1,
)
print("Training and generating prediction")
if n == 0:
train_grams = [gram_train_13_2]
test_grams = [gram_test_13_2]
y_pred = predict_first_set(train_grams, test_grams, y_train)
elif n == 1:
train_grams = [gram_train_13_2, gram_train_12_2, gram_train_13_3]
test_grams = [gram_test_13_2, gram_test_12_2, gram_test_13_3]
y_pred = predict_second_set(train_grams, test_grams, y_train)
else:
train_grams = [gram_train_13_2, gram_train_12_2, gram_train_13_3]
test_grams = [gram_test_13_2, gram_test_12_2, gram_test_13_3]
y_pred = predict_third_set(train_grams, test_grams, y_train)
y_pred = (y_pred + 1) / 2
y_pred_all += list(y_pred)
print("Saving prediction in CSV file")
with open(filename, "w") as csvfile:
fieldnames = ["Id", "Bound"]
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
for i in tqdm(range(0, len(y_pred_all))):
writer.writerow({"Id": i, "Bound": int(y_pred_all[i])})
print("You can find results on " + filename)
fieldmap = {
"congbio": "bioguide",
#"fec": "fec", # handled specially...
"govtrack":
"govtrack", # for sanity checking since we definitely have this already (I caught some Wikipedia errors)
"opensecrets": "opensecrets",
"votesmart": "votesmart",
"cspan": "cspan",
}
int_fields = ("govtrack", "votesmart", "cspan")
# default to not caching
cache = utils.flags().get('cache', False)
# Load legislator files and map bioguide IDs.
y1 = utils.load_data("legislators-current.yaml")
y2 = utils.load_data("legislators-historical.yaml")
bioguides = {}
for y in y1 + y2:
bioguides[y["id"]["bioguide"]] = y
# Okay now the Wikipedia stuff...
def get_matching_pages():
# Does a Wikipedia API search for pages containing either of the
# two templates. Returns the pages.
page_titles = set()
for template in ("CongLinks", "CongBio"):
def run_fix_mask(args, seed):
pruning.setup_seed(seed)
adj, features, labels, idx_train, idx_val, idx_test = load_data(
args['dataset'])
node_num = features.size()[0]
class_num = labels.numpy().max() + 1
adj = adj.cuda()
features = features.cuda()
labels = labels.cuda()
loss_func = nn.CrossEntropyLoss()
net_gcn = net.net_gcn(embedding_dim=args['embedding_dim'], adj=adj)
pruning.add_mask(net_gcn)
net_gcn = net_gcn.cuda()
print("load : {}".format(args['weight_dir']))
encoder_weight = {}
cl_ckpt = torch.load(args['weight_dir'], map_location='cuda')
encoder_weight['weight_orig_weight'] = cl_ckpt['gcn.fc.weight']
ori_state_dict = net_gcn.net_layer[0].state_dict()
ori_state_dict.update(encoder_weight)
net_gcn.net_layer[0].load_state_dict(ori_state_dict)
for name, param in net_gcn.named_parameters():
if 'mask' in name:
param.requires_grad = False
optimizer = torch.optim.Adam(net_gcn.parameters(),
lr=args['lr'],
weight_decay=args['weight_decay'])
acc_test = 0.0
best_val_acc = {'val_acc': 0, 'epoch': 0, 'test_acc': 0}
for epoch in range(args['total_epoch']):
optimizer.zero_grad()
output = net_gcn(features, adj)
loss = loss_func(output[idx_train], labels[idx_train])
loss.backward()
optimizer.step()
with torch.no_grad():
output = net_gcn(features, adj, val_test=True)
acc_val = f1_score(labels[idx_val].cpu().numpy(),
output[idx_val].cpu().numpy().argmax(axis=1),
average='micro')
acc_test = f1_score(labels[idx_test].cpu().numpy(),
output[idx_test].cpu().numpy().argmax(axis=1),
average='micro')
if acc_val > best_val_acc['val_acc']:
best_val_acc['val_acc'] = acc_val
best_val_acc['test_acc'] = acc_test
best_val_acc['epoch'] = epoch
print(
"(Fix Mask) Epoch:[{}] Val:[{:.2f}] Test:[{:.2f}] | Final Val:[{:.2f}] Test:[{:.2f}] at Epoch:[{}]"
.format(epoch, acc_val * 100, acc_test * 100,
best_val_acc['val_acc'] * 100,
best_val_acc['test_acc'] * 100, best_val_acc['epoch']))
return best_val_acc['val_acc'], best_val_acc['test_acc'], best_val_acc[
'epoch']
fn += 1 iff test_group[i][0] = M and classify(test+group[i] = B"""
test_group = test_group
FP, FN = 0, 0
size = len(test_group)
for i in range(size):
if self.root.find_class_by_example(
test_group[i]) == 'M' and test_group[i][0] == 'B':
FP += 1
elif self.root.find_class_by_example(
test_group[i]) == 'B' and test_group[i][0] == 'M':
FN += 1
loss = lost(FP, FN, size)
return loss
if __name__ == '__main__':
data = load_data("train.csv")
classifier = ID3(data)
classifier.train()
tester = load_data("test.csv")
classifier.test(tester, True)
"""loss calc"""
# loss = classifier.test_by_loss(tester)
# print(loss)
"""this is the experiment"""
# experiment("train.csv")
"""this is the accuracy check with M = 1"""
# classifier = ID3(data, 1)
# classifier.train()
# classifier.test(tester, True)
import project1 as p1
import utils
#-------------------------------------------------------------------------------
# Data loading. There is no need to edit code in this section.
#-------------------------------------------------------------------------------
train_data = utils.load_data('reviews_train.tsv')
# val_data = utils.load_data('reviews_val.tsv')
# test_data = utils.load_data('reviews_test.tsv')
train_texts, train_labels = zip(*((sample['text'], sample['sentiment']) for sample in train_data))
# val_texts, val_labels = zip(*((sample['text'], sample['sentiment']) for sample in val_data))
# test_texts, test_labels = zip(*((sample['text'], sample['sentiment']) for sample in test_data))
# dictionary = p1.bag_of_words(train_texts)
# dictionary_no_stopwords = p1.bag_of_words_removed_stopwords(train_texts)
# print("Length of Normal Dictionary:", len(dictionary), "\nLength of Dictionary Without Stopwords and Punc:", len(dictionary_no_stopwords))
# train_bow_features = p1.extract_bow_feature_vectors(train_texts, dictionary)
# val_bow_features = p1.extract_bow_feature_vectors(val_texts, dictionary)
# test_bow_features = p1.extract_bow_feature_vectors(test_texts, dictionary)
# # get the feature vectors with stopwords removed, punctuation removed, and words counted with frequency
# train_bow_features_no_stopwords = p1.extract_bow_feature_vectors_with_frequency(train_texts, dictionary_no_stopwords)
# val_bow_features_no_stopwords = p1.extract_bow_feature_vectors_with_frequency(val_texts, dictionary_no_stopwords)
# test_bow_features_no_stopwords = p1.extract_bow_feature_vectors_with_frequency(test_texts, dictionary_no_stopwords)
# # get the final features
model.summary()
x_input = tf.placeholder(tf.float32, [None, 224, 224, 3])
y = model(x_input)
t = tf.placeholder(tf.float32, [None, 10])
learning_rate = tf.placeholder(tf.float32, [])
cost = earth_mover_loss(t, y)
train = tf.train.MomentumOptimizer(learning_rate, 0.9).minimize(cost)
######################################################################################
from sklearn.utils import shuffle
from sklearn.metrics import precision_score, accuracy_score, f1_score, confusion_matrix
from sklearn.model_selection import train_test_split
from skimage.transform import resize
train_X_raw, train_y_raw = utils.load_data()
train_X_raw = train_X_raw
train_y_raw = train_y_raw
train_X_raw, test_X_raw, train_y_raw, test_y_raw = train_test_split(train_X_raw, train_y_raw, test_size=0.1, random_state=42)
test_X = np.zeros([0, image_size, image_size, 3])
for image in range(test_X_raw.shape[0]):
pic = test_X_raw[image]
img = resize(pic,(image_size,image_size,3))
temp = np.reshape(img, (1, image_size,image_size,3))
test_X = np.append(test_X, temp, axis=0)
test_y = test_y_raw
print('test size:')
print(test_X.shape)
def main(argv=None):
print("Loading training data..")
train_data = load_data(FLAGS.train_prefix, load_walks=True)
print("Done loading training data..")
train(train_data)
if __name__ == "__main__":
# argparse
args = get_train_args()
# check path_to_save existence
if os.path.exists(args.path_to_save_folder):
raise FileExistsError("save path folder already exists")
# set seed and device
set_global_seed(args.seed)
device = torch.device(args.device)
# load data
data = load_data(path=args.path_to_data, verbose=args.verbose)
# char2idx
char2idx = get_char2idx(data, verbose=args.verbose)
# dataset, collator, dataloader
train_dataset = LMDataset(
data,
char2idx,
max_length=args.max_length,
verbose=args.verbose,
)
train_collator = LMCollator(padding_value=char2idx[EOS], )
train_loader = DataLoader(
train_dataset,
batch_size=args.batch_size,
import os
import numpy as np
import random
from config import config_setting
from model import Model
from utils import load_data
from train import train
from torch import nn
if __name__ == '__main__':
cfg = config_setting()
train_loader, test_loader, features = load_data(cfg)
model = Model(features, cfg)
if cfg.use_cuda:
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
model = model.cuda()
print(model)
train(model, train_loader, test_loader, features, cfg)
def run():
# Field mapping. And which fields should be turned into integers.
# See https://en.wikipedia.org/wiki/Template:CongLinks for what's possibly available.
fieldmap = {
"congbio": "bioguide",
#"fec": "fec", # handled specially...
"govtrack":
"govtrack", # for sanity checking since we definitely have this already (I caught some Wikipedia errors)
"opensecrets": "opensecrets",
"votesmart": "votesmart",
"cspan": "cspan",
}
int_fields = ("govtrack", "votesmart", "cspan")
# default to not caching
cache = utils.flags().get('cache', False)
# Load legislator files and map bioguide IDs.
y1 = utils.load_data("legislators-current.yaml")
y2 = utils.load_data("legislators-historical.yaml")
bioguides = {}
for y in y1 + y2:
bioguides[y["id"]["bioguide"]] = y
# Okay now the Wikipedia stuff...
def get_matching_pages():
# Does a Wikipedia API search for pages containing either of the
# two templates. Returns the pages.
page_titles = set()
for template in ("CongLinks", "CongBio"):
eicontinue = ""
while True:
# construct query URL, using the "eicontinue" of the last query to get the next batch
url = 'http://en.wikipedia.org/w/api.php?action=query&list=embeddedin&eititle=Template:%s&eilimit=500&format=xml' % template
if eicontinue: url += "&eicontinue=" + eicontinue
# load the XML
print("Getting %s pages (%d...)" %
(template, len(page_titles)))
dom = lxml.etree.fromstring(utils.download(
url, None, True)) # can't cache eicontinue probably
for pgname in dom.xpath("query/embeddedin/ei/@title"):
page_titles.add(pgname)
# get the next eicontinue value and loop
eicontinue = dom.xpath(
"string(query-continue/embeddedin/@eicontinue)")
if not eicontinue: break
return page_titles
# Get the list of Wikipedia pages that use any of the templates we care about.
page_list_cache_file = os.path.join(utils.cache_dir(),
"legislators/wikipedia/page_titles")
if cache and os.path.exists(page_list_cache_file):
# Load from cache.
matching_pages = open(page_list_cache_file).read().split("\n")
else:
# Query Wikipedia API and save to cache.
matching_pages = get_matching_pages()
utils.write(("\n".join(matching_pages)), page_list_cache_file)
# Filter out things that aren't actually pages (User:, Talk:, etcetera, anything with a colon).
matching_pages = [p for p in matching_pages if ":" not in p]
# Load each page's content and parse the template.
for p in sorted(matching_pages):
if " campaign" in p: continue
if " (surname)" in p: continue
if "career of " in p: continue
if "for Congress" in p: continue
if p.startswith("List of "): continue
if p in ("New York in the American Civil War",
"Upper Marlboro, Maryland"):
continue
# Query the Wikipedia API to get the raw page content in XML,
# and then use XPath to get the raw page text.
url = "http://en.wikipedia.org/w/api.php?action=query&titles=" + urllib.parse.quote(
p.encode("utf8")) + "&export&exportnowrap"
cache_path = "legislators/wikipedia/pages/" + p
dom = lxml.etree.fromstring(utils.download(url, cache_path, not cache))
page_content = dom.xpath(
"string(mw:page/mw:revision/mw:text)",
namespaces={"mw": "http://www.mediawiki.org/xml/export-0.8/"})
# Build a dict for the IDs that we want to insert into our files.
new_ids = {
"wikipedia":
p # Wikipedia page name, with spaces for spaces (not underscores)
}
if "CongLinks" in page_content:
# Parse the key/val pairs in the template.
m = re.search(r"\{\{\s*CongLinks\s+([^}]*\S)\s*\}\}", page_content)
if not m: continue # no template?
for arg in m.group(1).split("|"):
if "=" not in arg: continue
key, val = arg.split("=", 1)
key = key.strip()
val = val.strip()
if val and key in fieldmap:
try:
if fieldmap[key] in int_fields: val = int(val)
except ValueError:
print("invalid value", key, val)
continue
if key == "opensecrets":
val = val.replace("&newMem=Y", "").replace(
"&newmem=Y", "").replace("&cycle=2004",
"").upper()
new_ids[fieldmap[key]] = val
if "bioguide" not in new_ids: continue
new_ids["bioguide"] = new_ids["bioguide"].upper() # hmm
bioguide = new_ids["bioguide"]
else:
m = re.search(r"\{\{\s*CongBio\s*\|\s*(\w+)\s*\}\}", page_content)
if not m: continue # no template?
bioguide = m.group(1).upper()
if not bioguide in bioguides:
print(
"Member not found: " + bioguide, p,
"(Might have been a delegate to the Constitutional Convention.)"
)
continue
# handle FEC ids specially because they are stored in an array...
fec_id = new_ids.get("fec")
if fec_id: del new_ids["fec"]
member = bioguides[bioguide]
member["id"].update(new_ids)
# ...finish the FEC id.
if fec_id:
if fec_id not in bioguides[bioguide]["id"].get("fec", []):
bioguides[bioguide]["id"].setdefault("fec", []).append(fec_id)
#print p.encode("utf8"), new_ids
utils.save_data(y1, "legislators-current.yaml")
utils.save_data(y2, "legislators-historical.yaml")
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {'dim': (23, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval', args=args)
utt2ark, utt2idx, all_list, utt2data = {}, {}, [], {}
for idx, kaldi_data_dir in enumerate(args.kaldi_data_dirs):
if not os.path.exists(args.emb_out_dirs[idx]):
os.makedirs(args.emb_out_dirs[idx])
feats_path = os.path.join(kaldi_data_dir, 'feats.scp')
vad_path = os.path.join(kaldi_data_dir, 'vad.scp')
assert os.path.exists(feats_path), 'Path `{}` does not exists.'.format(feats_path)
with open(feats_path) as f:
for line in f:
key, ark = line.split()
ark, position = ark.split(':')
input_tuple = (key, ark, int(position))
utt2data[key] = ut.load_data(input_tuple, mode='eval')
utt2idx[key] = idx
with open(vad_path) as f:
for line in f:
key, ark = line.split()
ark, position = ark.split(':')
vad_array = None
for ark_key, vec in kaldi_io.read_vec_flt_ark(ark):
if key == ark_key:
vad_array = np.array(vec, dtype=bool)
assert vad_array is not None
assert vad_array.size == utt2data[key].shape[1], 'Shapes does not fit: vad {}, mfcc {}'.format(
vad_array.size, utt2data[key].shape[1])
utt2data[key] = ut.apply_cmvn_sliding(utt2data[key]).T[vad_array]
# ==> load pre-trained model ???
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
print('==> successfully loaded model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(args.resume))
print('==> start testing.')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
for idx, utt in enumerate(utt2data):
embedding = network_eval.predict(utt2data[utt].T[np.newaxis, :, :, np.newaxis]).squeeze()
ut.write_txt_vectors(
os.path.join(args.emb_out_dirs[utt2idx[utt]], 'xvector.{}.txt'.format(idx)), {utt: embedding})
from keras.callbacks import EarlyStopping, ModelCheckpoint, CSVLogger
from keras.optimizers import SGD
from models import simple_CNN
from utils import load_data, preprocess_input
import keras.backend as K
import tensorflow as tf
data_path = '../datasets/fer2013/fer2013.csv'
model_save_path = '../trained_models/simpler_CNN.hdf5'
faces, emotions = load_data(data_path)
faces = preprocess_input(faces)
num_classes = emotions.shape[1]
image_size = faces.shape[1:]
batch_size = 128
num_epochs = 1000
model = simple_CNN(image_size, num_classes)
model.compile(optimizer='adam', loss='categorical_crossentropy',
metrics=['accuracy'])
csv_logger = CSVLogger('training.log')
early_stop = EarlyStopping('val_acc',patience=200,verbose=1)
model_checkpoint = ModelCheckpoint(model_save_path,
'val_acc', verbose=1,
save_best_only=True)
model_callbacks = [early_stop, model_checkpoint, csv_logger]
#keras bug
K.get_session().run(tf.global_variables_initializer())
model.fit(faces,emotions,batch_size,num_epochs,verbose=1,
callbacks=model_callbacks,
from deepy.trainers import SGDTrainer, LearningRateAnnealer, AdamTrainer
from deepy.layers import LSTM
from layers import FullOutputLayer
logging.basicConfig(level=logging.INFO)
default_model = os.path.join(os.path.dirname(__file__), "models",
"lstm_rnnlm.gz")
if __name__ == '__main__':
ap = ArgumentParser()
ap.add_argument("--model", default="")
ap.add_argument("--small", action="store_true")
args = ap.parse_args()
vocab, lmdata = load_data(small=args.small, history_len=5, batch_size=64)
model = NeuralLM(vocab.size)
model.stack(
LSTM(hidden_size=100,
output_type="sequence",
persistent_state=True,
batch_size=lmdata.size,
reset_state_for_input=0), FullOutputLayer(vocab.size))
if os.path.exists(args.model):
model.load_params(args.model)
trainer = SGDTrainer(
model, {
"learning_rate": LearningRateAnnealer.learning_rate(1.2),
"weight_l2": 1e-7
# parse training arguments
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type = int, default = 10000, help = 'Number of epochs to train.')
parser.add_argument('--lr', type = float, default = 0.005, help = 'Initial learning rate.')
parser.add_argument('--weight_decay', type = float, default = 5e-4, help = 'Weight decay (L2 loss on parameters).')
parser.add_argument('--hidden', type = int, default = 8, help = 'Number of hidden units.')
parser.add_argument('--n_heads', type = int, default = 8, help = 'Number of head attentions.')
parser.add_argument('--dropout', type = float, default = 0.6, help = 'Dropout rate (1 - keep probability).')
parser.add_argument('--alpha', type = float, default = 0.2, help = 'Alpha for the leaky_relu.')
parser.add_argument('--patience', type = int, default = 100, help = 'Patience')
args = parser.parse_args()
args.use_cuda = torch.cuda.is_available()
# load data
adj, features, labels, idx_train, idx_val, idx_test = load_data()
model = GAT(n_input = features.shape[1], n_hidden = args.hidden,
n_classes = int(labels.max()) + 1, dropout = args.dropout,
alpha = args.alpha, n_heads = args.n_heads)
if args.use_cuda:
model.cuda()
features = features.cuda()
adj = adj.cuda()
labels = labels.cuda()
idx_train = idx_train.cuda()
idx_val = idx_val.cuda()
idx_test = idx_test.cuda()
shuffle=data_type == "train",
drop_last=False)
data_loader = DataLoader(dataset,
batch_sampler=sampler,
collate_fn=EdgeSeqDataset.batchify,
pin_memory=data_type == "train")
data_loaders[data_type] = data_loader
logger.info("data (data_type: {:<5s}, len: {}) generated".format(
data_type, len(dataset.data)))
logger.info(
"data_loader (data_type: {:<5s}, len: {}, batch_size: {}) generated"
.format(data_type, len(data_loader),
finetune_config["batch_size"]))
else:
data = load_data(finetune_config["graph_dir"],
finetune_config["pattern_dir"],
finetune_config["metadata_dir"],
num_workers=finetune_config["num_workers"])
logger.info("{}/{}/{} data loaded".format(len(data["train"]),
len(data["dev"]),
len(data["test"])))
for data_type, x in data.items():
if finetune_config["model"] in ["RGCN", "RGIN", "RSIN"]:
if os.path.exists(
os.path.join(finetune_config["save_data_dir"],
"%s_dgl_dataset.pt" % (data_type))):
dataset = GraphAdjDataset(list())
dataset.load(
os.path.join(finetune_config["save_data_dir"],
"%s_dgl_dataset.pt" % (data_type)))
else:
dataset = GraphAdjDataset(x)
prompt += ('====================================\n')
print(prompt, end='')
f = open('{0}/opt.txt'.format(run_dir), 'w')
f.write(prompt)
f.close()
if torch.cuda.is_available():
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = "{0}".format(opt.gpu)
torch.cuda.set_device(opt.gpu)
# device = torch.device('cuda:{0}'.format(opt.gpu))
# Configure data loader
import utils
trainset, trainset2, testset = utils.load_data(opt=opt)
train_loader = torch.utils.data.DataLoader(trainset,
batch_size=opt.batch_size,
drop_last=True,
sampler=InfiniteSampler(
len(trainset))) # model
train_loader2 = torch.utils.data.DataLoader(trainset2,
batch_size=opt.batch_size,
drop_last=True,
sampler=InfiniteSampler(
len(trainset2))) # model
test_loader = torch.utils.data.DataLoader(testset,
batch_size=opt.batch_size,
shuffle=True,
drop_last=True) # model
trend_rdd = yeargrowth_rdd \
.map(lambda row: ((row[0][0], row[0][1]),
str(row[0][2]) + ":" + utils.prettify_growth(row[1]))) \
.groupByKey() \
.map(lambda row: (row[1], (row[0]))) \
.cache()
# .mapValues(iterate) \
# .mapValues(iterate) put it after groupByKey to see content of trend
similartrendingcompanies_rdd = trend_rdd.join(trend_rdd) \
.filter(filter_couples) \
.collect()
# sort values and remove duplicates (A,B)(B,A))
# .map(lambda row: (row[0], tuple(sorted(row[1])))).distinct()
# it seems that the rdd already does combinations
for kv in similartrendingcompanies_rdd:
print(kv)
if __name__ == "__main__":
spark = utils.create_session("job2")
sc = spark.sparkContext
# sqlContext = SQLContext(sc)
history_rdd = utils.load_data(spark, HISTORY_PATH, preview=False)
legend_rdd = utils.load_data(spark, LEGEND_PATH, preview=False)
run_job(history_rdd, legend_rdd)
def main():
# gpu configuration
toolkits.initialize_GPU(args)
import model
# ==================================
# Get Train/Val.
# ==================================
print('==> calculating test({}) data lists...'.format(args.test_type))
publicTest = pd.read_csv("/content/VoveDataset/public-test.csv")
list1 = addPath(np.array(publicTest["audio_1"]))
list2 = addPath(np.array(publicTest["audio_2"]))
total_list = np.concatenate((list1, list2))
unique_list = np.unique(total_list)
# ==================================
# Get Model
# ==================================
# construct the data generator.
params = {
'dim': (257, None, 1),
'nfft': 512,
'spec_len': 250,
'win_length': 400,
'hop_length': 160,
'n_classes': 5994,
'sampling_rate': 16000,
'normalize': True,
}
network_eval = model.vggvox_resnet2d_icassp(input_dim=params['dim'],
num_class=params['n_classes'],
mode='eval',
args=args)
# ==> load pre-trained model ???
if args.resume:
# ==> get real_model from arguments input,
# load the model if the imag_model == real_model.
if os.path.isfile(args.resume):
network_eval.load_weights(os.path.join(args.resume), by_name=True)
result_path = "/content/VGG-Speaker-Recognition/result"
print('==> successfully loading model {}.'.format(args.resume))
else:
raise IOError("==> no checkpoint found at '{}'".format(
args.resume))
else:
raise IOError('==> please type in the model to load')
print('==> start testing.')
# The feature extraction process has to be done sample-by-sample,
# because each sample is of different lengths.
total_length = len(unique_list)
feats, scores, labels = [], [], []
for c, ID in enumerate(pbar(unique_list)):
specs = ut.load_data(ID,
win_length=params['win_length'],
sr=params['sampling_rate'],
hop_length=params['hop_length'],
n_fft=params['nfft'],
spec_len=params['spec_len'],
mode='eval')
specs = np.expand_dims(np.expand_dims(specs, 0), -1)
v = network_eval.predict(specs)
feats += [v]
feats = np.array(feats)
np.save("/content/feats.npy", feats)
preds_train = aclf.predict(x_train)
return train_accuracy, test_accuracy, f1(y_train, preds_train), f1(y_test, preds)
###################################################
# Modify for running your experiments accordingly #
###################################################
if __name__ == '__main__':
args = load_args()
accuracy_train = []
f1_tscore = []
accuracy = []
f1_score = []
x_axis = np.arange(1, 26)
max_features = [1,2,5,8,10,20,25,35,50]
# x_axis = np.arange(10,220,10)
random = np.arange(1,11)
x_train, y_train, x_test, y_test = load_data(args.root_dir)
L = np.arange(10,210,10)
if args.county_dict == 1:
county_info(args)
if args.decision_tree == 1:
for x in range(1,126):
train_acc, test_acc, f1_train, f1_test = decision_tree_testing(x_train, y_train, x_test, y_test,x)
accuracy_train.append(train_acc)
accuracy.append(test_acc)
f1_tscore.append(f1_train)
f1_score.append(f1_test)
plt.plot(x_axis, accuracy, label="Testing Accuracy")
plt.plot(x_axis, f1_score, label = "Training F1 Score")
plt.plot(x_axis, accuracy_train, label = "Training Accuracy")
plt.plot(x_axis, f1_tscore, label = "Testing F1 score")
plt.ylabel("Accuracy")
for proposal_folder in proposal_folders:
fn_clusters = sorted(glob.glob(os.path.join(proposal_folder, fn_node_pattern)))
proposals.extend([fn_node for fn_node in fn_clusters])
assert len(proposals) == len(probs)
pos_lst = []
for idx, prob in enumerate(probs):
if prob < args.th_pos:
continue
pos_lst.append([idx, prob])
pos_lst = sorted(pos_lst, key=lambda x:x[1], reverse=True)
# get all clusters
clusters = []
for idx, _ in tqdm(pos_lst):
cluster = load_data(proposals[idx])
clusters.append(cluster)
idx2lb, idx2lbs = nms(clusters, args.th_iou)
# output stats
multi_lb_num = 0
for _, lbs in idx2lbs.items():
if len(lbs) > 1:
multi_lb_num += 1
inst_num = len(idx2lb)
cls_num = len(set(idx2lb.values()))
print('#inst: {}, #class: {}, #multi-label: {}'.format(inst_num, cls_num, multi_lb_num))
print('#inst-coverage: {:.2f}'.format(1. * inst_num / tot_inst_num))
from config import args
from utils import load_data, build_vocab, gen_submission, gen_final_submission, eval_based_on_outputs
from model import Model
if __name__ == '__main__':
if not args.pretrained:
print('No pretrained model specified.')
exit(0)
build_vocab()
if args.test_mode:
dev_data = load_data('../data/test-data-processed.json')
else:
dev_data = load_data('../data/dev-data-processed.json')
model_path_list = args.pretrained.split(',')
for model_path in model_path_list:
print('Load model from %s...' % model_path)
args.pretrained = model_path
model = Model(args)
# evaluate on development dataset
dev_acc = model.evaluate(dev_data)
print('dev accuracy: %f' % dev_acc)
# generate submission zip file for Codalab
prediction = model.predict(dev_data)
gen_submission(dev_data, prediction)
gen_final_submission(dev_data)
eval_based_on_outputs('./answer.txt')