import utils
import os
from argparse import ArgumentParser
arguments = ArgumentParser()
arguments.add_argument('--base', type=str, default='')
arguments.add_argument('--pattern', type=str, default='')
args = arguments.parse_args()
base_dir = args.base
merged_res = {}
for i in range(100):
try:
res = utils.read_pickle(
os.path.join(base_dir, '{0}_{1}'.format(args.pattern, i)))
except:
continue
for k, v in res.iteritems():
merged_res[k] = v
utils.write_pickle(merged_res,
os.path.join(base_dir, '{0}_merged'.format(args.pattern)))
def load_data():
return utils.read_pickle('train_data.pkl'), utils.read_pickle(
'test_data.pkl')
def run(seed):
# create folders for scores models and preds
folder_models = './models/domain1_var1/scores/'
if not os.path.exists(folder_models):
os.makedirs(folder_models)
folder_preds = './predicts/domain1_var1/scores/'
if not os.path.exists(folder_preds):
os.makedirs(folder_preds)
print('Loading data...')
# load biases
ic_bias = read_pickle('./data/biases/ic_biases.pickle')
ic_bias_site = read_pickle('./data/biases/ic_biases_site.pickle')
fnc_bias = read_pickle('./data/biases/fnc_biases.pickle')
fnc_bias_site = read_pickle('./data/biases/fnc_biases_site.pickle')
pca_bias = read_pickle('./data/biases/200pca_biases.pickle')
pca_bias_site = read_pickle('./data/biases/200pca_biases_site.pickle')
# load classifier and add extra sites2
extra_site = pd.DataFrame()
extra_site['Id'] = np.load('./predicts/classifier/site2_test_new_9735.npy')
# load competiton data
ids_df = pd.read_csv('./data/raw/reveal_ID_site2.csv')
fnc_df = pd.read_csv('./data/raw/fnc.csv')
loading_df = pd.read_csv('./data/raw/loading.csv')
labels_df = pd.read_csv('./data/raw/train_scores.csv')
ids_df = ids_df.append(extra_site)
print('Detected Site2 ids count: ', ids_df['Id'].nunique())
# load created features
agg_df = pd.read_csv('./data/features/agg_feats.csv')
im_df = pd.read_csv('./data/features/im_feats.csv')
dl_df = pd.read_csv('./data/features/dl_feats.csv')
pca_df = pd.read_csv('./data/features/200pca_feats/200pca_3d_k0.csv')
for i in range(1, 6):
part = pd.read_csv(
'./data/features/200pca_feats/200pca_3d_k{}.csv'.format(i))
del part['Id']
pca_df = pd.concat((pca_df, part), axis=1)
# merge data
ic_cols = list(loading_df.columns[1:])
fnc_cols = list(fnc_df.columns[1:])
agg_cols = list(agg_df.columns[1:])
im_cols = list(im_df.columns[1:])
pca_cols = list(pca_df.columns[1:])
dl_cols = list(dl_df.columns[1:])
df = fnc_df.merge(loading_df, on='Id')
df = df.merge(agg_df, how='left', on='Id')
df = df.merge(im_df, how='left', on='Id')
df = df.merge(pca_df, how='left', on='Id')
df = df.merge(dl_df, how='left', on='Id')
df = df.merge(labels_df, how='left', on='Id')
del loading_df, fnc_df, agg_df, im_df, pca_df
gc.collect()
# split train and test
df.loc[df['Id'].isin(labels_df['Id']), 'is_test'] = 0
df.loc[~df['Id'].isin(labels_df['Id']), 'is_test'] = 1
train = df.query('is_test==0')
del train['is_test']
test = df.query('is_test==1')
del test['is_test']
y = train['domain1_var1'].copy().reset_index(drop=True)
d11_index = list(train['domain1_var1'].dropna().index)
# apply biases
for c in ic_bias_site.keys():
test.loc[~test['Id'].isin(ids_df['Id']), c] += ic_bias[c]
test.loc[test['Id'].isin(ids_df['Id']), c] += ic_bias_site[c]
for c in fnc_bias_site.keys():
test.loc[~test['Id'].isin(ids_df['Id']), c] += fnc_bias[c]
test.loc[test['Id'].isin(ids_df['Id']), c] += fnc_bias_site[c]
for c in pca_bias_site.keys():
test.loc[~test['Id'].isin(ids_df['Id']), c] += pca_bias[c]
test.loc[test['Id'].isin(ids_df['Id']), c] += pca_bias_site[c]
# save df for scaling
df_scale = pd.concat([train, test], axis=0)
# I. Create fnc score
print('Creating FNC score...')
# prepare datasets for fnc score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, fnc_cols)
# define models
names = ['ENet', 'BRidge']
names = [name + '_fnc_seed{}'.format(seed) for name in names]
pack = [
ElasticNet(alpha=0.05, l1_ratio=0.5, random_state=0),
BayesianRidge()
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 2, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 2, names)
# save oof, pred, models
np.save(folder_preds + 'fnc_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'fnc_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# II. Create agg score
print('Creating AGG score...')
# prepare datasets for agg score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, agg_cols)
# define models
names = ['ENet', 'Huber']
names = [name + '_agg_seed{}'.format(seed) for name in names]
pack = [
ElasticNet(alpha=0.05, l1_ratio=0.3, random_state=0),
HuberRegressor(epsilon=2.5, alpha=1)
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 2, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 2, names)
# save oof, pred, models
np.save(folder_preds + 'agg_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'agg_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# III. Create pca score
print('Creating PCA score...')
# prepare datasets for pca score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, pca_cols)
# define models
names = ['ENet', 'BRidge']
names = [name + '_pca_seed{}'.format(seed) for name in names]
pack = [
ElasticNet(alpha=0.2, l1_ratio=0.2, random_state=0),
BayesianRidge()
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 2, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 2, names)
# save oof, pred, models
np.save(folder_preds + 'pca_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'pca_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# IV. Create im score
print('Creating IM score...')
# prepare datasets for pca score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, im_cols)
# define models
names = ['ENet', 'BRidge']
names = [name + '_im_seed{}'.format(seed) for name in names]
pack = [
ElasticNet(alpha=0.2, l1_ratio=0.2, random_state=0),
BayesianRidge()
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 2, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 2, names)
# save oof, pred, models
np.save(folder_preds + 'im_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'im_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# V. Create dl score
print('Creating DL score...')
# prepare datasets for pca score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, dl_cols)
# define models
names = ['ENet', 'BRidge']
names = [name + '_dl_seed{}'.format(seed) for name in names]
pack = [
ElasticNet(alpha=0.2, l1_ratio=0.2, random_state=0),
BayesianRidge()
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 2, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 2, names)
# save oof, pred, models
np.save(folder_preds + 'dl_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'dl_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# VI. Training and predicting procedure
print('Training has started...')
print('Reading scores from ', folder_preds)
# add scores
for prefix in ['fnc', 'agg', 'im', 'pca', 'dl']:
train.loc[d11_index, prefix + '_score'] = np.load(
folder_preds + '{}_score_seed{}.npy'.format(prefix, seed))
test.loc[:, prefix + '_score'] = np.load(
folder_preds + '{}_score_test_seed{}.npy'.format(prefix, seed))
score_cols = [c for c in train.columns if c.endswith('_score')]
# save df for scaling
df_scale = pd.concat([train, test], axis=0)
# create differents datasets
# linear
linear_cols = sorted(
list(set(ic_cols + fnc_cols + pca_cols) - set(['IC_20'])))
train_linear, test_linear = scale_select_data(train, test, df_scale,
linear_cols)
# kernel
kernel_cols = sorted(list(set(ic_cols + pca_cols) - set(['IC_20'])))
train_kernel, test_kernel = scale_select_data(train=train,
test=test,
df_scale=df_scale,
cols=kernel_cols,
scale_factor=0.2,
scale_cols=pca_cols,
sc=MinMaxScaler())
# score
sc_cols = sorted(list(set(ic_cols + score_cols) - set(['IC_20'])))
train_sc, test_sc = scale_select_data(train, test, df_scale, sc_cols)
# learning process on different datasets
names = ['GP', 'SVM1', 'SVM2', 'OMP', 'KR']
names = [name + '_seed{}'.format(seed) for name in names]
pack = [
GaussianProcessRegressor(DotProduct(), random_state=0),
NuSVR(C=5, kernel='rbf'),
NuSVR(C=5, kernel='rbf'),
OrthogonalMatchingPursuitCV(),
KernelRidge(kernel='poly', degree=2, alpha=10)
]
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_sc] * 2 + [train_kernel] + [train_linear] * 2, y)
de_blend = zoo.blend_oof()
preds = zoo.predict([test_sc] * 2 + [test_kernel] + [test_linear] * 2,
names,
is_blend=True)
# rewrite folders for models and preds
folder_models = './models/domain1_var1/stack/'
if not os.path.exists(folder_models):
os.makedirs(folder_models)
folder_preds = './predicts/domain1_var1/stack/'
if not os.path.exists(folder_preds):
os.makedirs(folder_preds)
print('Saving models to', folder_models)
print('Saving predictions to', folder_preds)
# save oofs and models
zoo.save_oofs(names, folder=folder_preds)
zoo.save_models(names, folder=folder_models)
# stacking predictions
print('Stacking predictions...')
d11_prediction = pd.DataFrame()
d11_prediction['Id'] = test['Id'].values
d11_prediction['pred'] = preds
d11_prediction.to_csv(folder_preds +
'domain1_var1_stack_seed{}.csv'.format(seed),
index=False)
print('domain1_var1 seed pred is saved as',
folder_preds + 'domain1_var1_stack_seed{}.csv'.format(seed))
import pickle
import pandas as pd
from stattools import grangercausalitytests
from datetime import datetime
import utils
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.DEBUG)
arguments = ArgumentParser()
arguments.add_argument('--path', type=str, default="flattened-timeseries")
arguments.add_argument('--index', type=int)
args = arguments.parse_args()
results = {}
lags = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 21, 30, 60, 90]
df = utils.read_pickle(args.path)
def pairwise_granger(words):
global df
(word1, word2) = words
if word1 not in df or word2 not in df:
return (word1, word2, None)
best_lag, res = grangercausalitytests(numpy.transpose(
[df[word2], df[word1]]),
lags,
verbose=False)
causal_lag = None
f_pvalue = res[0]['params_ftest'][1]
lr_pvalue = res[0]['lrtest'][1]
if f_pvalue < 0.01 and lr_pvalue < 0.01:
# Sample images for learners.
def sample_zip(fn_in, fn_out, rate=0.01, seed=42):
np.random.seed(seed)
with zipfile.ZipFile(fn_in) as fin, zipfile.ZipFile(fn_out, "w") as fout:
sampled = filter(lambda _: np.random.rand() < rate, fin.filelist)
for zInfo in sampled:
fout.writestr(zInfo, fin.read(zInfo))
sample_zip("train2014.zip", "train2014_sample.zip")
sample_zip("val2014.zip", "val2014_sample.zip")
# Load prepared embeddings.
train_img_embeds = utils.read_pickle("train_img_embeds.pickle")
train_img_fns = utils.read_pickle("train_img_fns.pickle")
val_img_embeds = utils.read_pickle("val_img_embeds.pickle")
val_img_fns = utils.read_pickle("val_img_fns.pickle")
# Check shapes.
print(train_img_embeds.shape, len(train_img_fns))
print(val_img_embeds.shape, len(val_img_fns))
# Extract captions.
def get_captions_for_fns(fns, zip_fn, zip_json_path):
zf = zipfile.ZipFile(zip_fn)
j = json.loads(zf.read(zip_json_path).decode("utf8"))
id_to_fn = {img["id"]: img["file_name"] for img in j["images"]}
fn_to_caps = defaultdict(list)
cv2.waitKey(0)
cv2.destroyAllWindows()
if __name__ == '__main__':
args = utils.get_options()
input_image_dirpath = osp.join(osp.dirname(__file__), args.in_dir)
# recognize any extentions
image_paths, image_names = utils.get_file_paths(input_image_dirpath, "*")
# read camera parameters
camera_param_filepath = osp.join(osp.dirname(__file__),
args.camera_param_path)
cameraMatrix, distCoeffs, rvecs, tvecs, stdDevIn, stdDevEx = \
utils.read_pickle(camera_param_filepath)
# read parameters from arguments
dictionary = utils.get_aruco_dict(args.aruco_dict)
squareL = args.square_length
markerL = args.marker_length
tb = args.v_margin
lr = args.h_margin
pixels_per_mm = args.pixels_per_mm
# read parameters from configuration pickle file
if args.input_board_cfg_pkl:
board_cfg_pkl_path = osp.join(osp.dirname(__file__),
args.board_cfg_pkl_path)
board_cfg = utils.read_pickle(board_cfg_pkl_path)
dictionary = utils.get_aruco_dict(board_cfg['dict_label'])
squareL = board_cfg['square_length']
def train(args):
#for creating the visdom object
DEFAULT_PORT = 8097
DEFAULT_HOSTNAME = "http://localhost"
viz = Visdom(DEFAULT_HOSTNAME, DEFAULT_PORT, ipv6=False)
hyparam_list = [
("model", args.model_name),
("cube", args.cube_len),
("bs", args.batch_size),
("g_lr", args.g_lr),
("d_lr", args.d_lr),
("z", args.z_dis),
("bias", args.bias),
("sl", args.soft_label),
]
hyparam_dict = OrderedDict(((arg, value) for arg, value in hyparam_list))
log_param = make_hyparam_string(hyparam_dict)
print(log_param)
# for using tensorboard
if args.use_tensorboard:
import tensorflow as tf
summary_writer = tf.summary.FileWriter(args.output_dir + args.log_dir +
log_param)
def inject_summary(summary_writer, tag, value, step):
summary = tf.Summary(
value=[tf.Summary.Value(tag=tag, simple_value=value)])
summary_writer.add_summary(summary, global_step=step)
inject_summary = inject_summary
# datset define
dsets_path = args.input_dir + args.data_dir + "train/"
print(dsets_path)
x_train = np.load("voxels_3DMNIST_16.npy")
dataset = x_train.reshape(-1,
args.cube_len * args.cube_len * args.cube_len)
print(dataset.shape)
dset_loaders = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
# model define
D = _D(args)
G = _G(args)
D_solver = optim.Adam(D.parameters(), lr=args.d_lr, betas=args.beta)
G_solver = optim.Adam(G.parameters(), lr=args.g_lr, betas=args.beta)
if torch.cuda.is_available():
print("using cuda")
D.cuda()
G.cuda()
criterion = nn.BCELoss()
pickle_path = "." + args.pickle_dir + log_param
read_pickle(pickle_path, G, G_solver, D, D_solver)
for epoch in range(args.n_epochs):
epoch_start_time = time.time()
print("epoch %d started" % (epoch))
for i, X in enumerate(dset_loaders):
X = var_or_cuda(X)
X = X.type(torch.cuda.FloatTensor)
if X.size()[0] != int(args.batch_size):
#print("batch_size != {} drop last incompatible batch".format(int(args.batch_size)))
continue
Z = generateZ(args)
real_labels = var_or_cuda(torch.ones(args.batch_size)).view(
-1, 1, 1, 1, 1)
fake_labels = var_or_cuda(torch.zeros(args.batch_size)).view(
-1, 1, 1, 1, 1)
if args.soft_label:
real_labels = var_or_cuda(
torch.Tensor(args.batch_size).uniform_(0.9, 1.1)).view(
-1, 1, 1, 1, 1) ####
#fake_labels = var_or_cuda(torch.Tensor(args.batch_size).uniform_(0, 0.3)).view(-1,1,1,1,1)
fake_labels = var_or_cuda(torch.zeros(args.batch_size)).view(
-1, 1, 1, 1, 1) #####
# ============= Train the discriminator =============#
d_real = D(X)
d_real_loss = criterion(d_real, real_labels)
fake = G(Z)
d_fake = D(fake)
d_fake_loss = criterion(d_fake, fake_labels)
d_loss = d_real_loss + d_fake_loss
d_real_acu = torch.ge(d_real.squeeze(), 0.5).float()
d_fake_acu = torch.le(d_fake.squeeze(), 0.5).float()
d_total_acu = torch.mean(torch.cat((d_real_acu, d_fake_acu), 0))
#if 1:
if d_total_acu <= args.d_thresh:
D.zero_grad()
d_loss.backward()
D_solver.step()
# =============== Train the generator ===============#
Z = generateZ(args)
fake = G(Z)
d_fake = D(fake)
g_loss = criterion(d_fake, real_labels)
D.zero_grad()
G.zero_grad()
g_loss.backward()
G_solver.step()
#######
#print(fake.shape)
#print(fake.cpu().data[:8].squeeze().numpy().shape)
# =============== logging each iteration ===============#
iteration = str(G_solver.state_dict()['state'][
G_solver.state_dict()['param_groups'][0]['params'][0]]['step'])
#print(type(iteration))
#iteration = str(i)
#saving the model and a image each 100 iteration
if int(iteration) % 300 == 0:
#pickle_save_path = args.output_dir + args.pickle_dir + log_param
#save_new_pickle(pickle_save_path, iteration, G, G_solver, D, D_solver)
samples = fake.cpu().data[:8].squeeze().numpy()
#print(samples.shape)
for s in range(8):
plotVoxelVisdom(samples[s, ...], viz,
"Iteration:{:.4}".format(iteration))
# image_path = args.output_dir + args.image_dir + log_param
# if not os.path.exists(image_path):
# os.makedirs(image_path)
# SavePloat_Voxels(samples, image_path, iteration)
# =============== each epoch save model or save image ===============#
print(
'Iter-{}; , D_loss : {:.4}, G_loss : {:.4}, D_acu : {:.4}, D_lr : {:.4}'
.format(iteration, d_loss.item(), g_loss.item(),
d_total_acu.item(),
D_solver.state_dict()['param_groups'][0]["lr"]))
epoch_end_time = time.time()
if (epoch + 1) % args.image_save_step == 0:
samples = fake.cpu().data[:8].squeeze().numpy()
image_path = args.output_dir + args.image_dir + log_param
if not os.path.exists(image_path):
os.makedirs(image_path)
SavePloat_Voxels(samples, image_path, iteration)
if (epoch + 1) % args.pickle_step == 0:
pickle_save_path = args.output_dir + args.pickle_dir + log_param
save_new_pickle(pickle_save_path, iteration, G, G_solver, D,
D_solver)
print("epoch time", (epoch_end_time - epoch_start_time) / 60)
print("epoch %d ended" % (epoch))
print("################################################")
# # sample images for faster training
# def sample_zip(fn_in, fn_out, rate=0.01, seed=42):
# np.random.seed(seed)
# with zipfile.ZipFile(fn_in) as fin, zipfile.ZipFile(fn_out, "w") as fout:
# sampled = filter(lambda _: np.random.rand() < rate, fin.filelist)
# for zInfo in sampled:
# fout.writestr(zInfo, fin.read(zInfo))
# sample_zip(train2014_zip, "../data/coco/train2014_sample_yoloV2.zip", rate = 0.01, seed = 42)
# sample_zip(val2014_zip, "../data/coco/val2014_sample_yoloV2.zip", rate = 0.01, seed = 42)
# In[12]:
# load prepared embeddings
train_img_embeds = utils.read_pickle(
"../data/coco/extracted/train_img_embeds_yoloV2_{}.pickle".format(action))
train_img_fns = utils.read_pickle(
"../data/coco/extracted/train_img_fns_yoloV2_{}.pickle".format(action))
val_img_embeds = utils.read_pickle(
"../data/coco/extracted/val_img_embeds_yoloV2_{}.pickle".format(action))
val_img_fns = utils.read_pickle(
"../data/coco/extracted/val_img_fns_yoloV2_{}.pickle".format(action))
# check shapes
print("training data: ", train_img_embeds.shape, len(train_img_fns))
print("valicatoin data: ", val_img_embeds.shape, len(val_img_fns))
# In[13]:
# check prepared samples of images
list(filter(lambda x: x.endswith("_sample_yoloV2.zip"), os.listdir(".")))
parser.add_argument(
'--size', type=int,
nargs='?', default=32, help='the embedding size')
parser.add_argument(
'--num_walks', type=int,
nargs='?', default=10, help='the number of random walks to originate from each vertex')
parser.add_argument(
'--walk_length', type=int,
nargs='?', default=80, help='the length of each random walk')
args = parser.parse_args()
size = args.size
print 'learning embeddings of dimension {}'.format(args.size)
x = utils.read_pickle(args.x_path[0])
g = BipartiteGraph(x)
print 'walk path: {}'.format(args.walk_path)
print 'x path: {}'.format(args.x_path)
if args.walk_path == "":
print 'generating new random walk dataset'
print 'building edges'
g.build_edge_array()
print 'generating walks'
walks = g.generate_walks(args.num_walks, args.walk_length)
df = pd.DataFrame(walks)
walk_path = 'local_resources/walks_thresh10_num_{}_length_{}'.format(args.num_walks, args.walk_length)
df.to_csv(walk_path, index=False, header=None)
else:
print 'learning embeddings'
walks = pd.read_csv(args.walk_path,
def main(game, level, player_img, use_graph, draw_all_labels, draw_dup_labels, draw_path, show_score):
# Create the Level
level_obj = Level.generate_level_from_file(game, level)
# Level saved files
state_graph_file = "level_saved_files_%s/enumerated_state_graphs/%s/%s.gpickle" % (player_img, game, level)
if game == "generated" and os.path.exists("level_saved_files_%s/generated_level_paths/%s.pickle" % (player_img, level)):
generated_level_path_coords = read_pickle("level_saved_files_%s/generated_level_paths/%s.pickle" % (player_img, level))
else:
generated_level_path_coords = None
if use_graph and os.path.exists(state_graph_file):
print("***** USING ENUMERATED STATE GRAPH *****")
state_graph = nx.read_gpickle(state_graph_file)
else:
print("***** USING MANUAL CONTROLS *****")
state_graph = None
edge_actions_dict = None if state_graph is None else nx.get_edge_attributes(state_graph, 'action')
# Background
FPS = 40 # frame rate
ANI = 4 # animation cycles
WORLD_X = min(level_obj.width, MAX_WIDTH)
WORLD_Y = min(level_obj.height, MAX_HEIGHT)
clock = pygame.time.Clock()
pygame.init()
world = pygame.display.set_mode([WORLD_X, WORLD_Y])
BACKGROUND_COLOR = COLORS.get('DARK_GRAY')
# Player
player_model = Player(player_img, level_obj)
player_view = PlayerView(player_img)
player_list = pygame.sprite.Group()
player_list.add(player_view)
# Level
platform_sprites = get_sprites(level_obj.get_platform_coords(), 'block_tile.png')
goal_sprites = get_sprites(level_obj.get_goal_coords(), 'goal_tile.png')
bonus_sprites = get_sprites(level_obj.get_bonus_coords(), 'bonus_tile.png')
one_way_platform_sprites = get_sprites(level_obj.get_one_way_platform_coords(), 'one_way_block_tile.png')
hazard_sprites = get_sprites(level_obj.get_hazard_coords(), 'hazard_tile.png')
wall_sprites = get_sprites(level_obj.get_wall_coords(), 'block_tile.png')
collected_bonus_tile_coords_dict = {}
# Camera
camera = Camera(Camera.camera_function, level_obj.width, level_obj.height, WORLD_X, WORLD_Y)
# Setup drawing metatile labels
if draw_all_labels or draw_dup_labels:
metatile_labels, font_color, label_padding = \
setup_metatile_labels(game, level, player_img, draw_all_labels, draw_dup_labels)
# Setup drawing solution path
if draw_path:
path_font_color = COLORS.get('GREEN')
start_font_color = COLORS.get('BLUE')
goal_font_color = COLORS.get('RED')
if generated_level_path_coords is not None:
path_coords = generated_level_path_coords
start_coord = generated_level_path_coords[0]
goal_coord = generated_level_path_coords[-1]
elif os.path.exists(state_graph_file):
graph = nx.read_gpickle(state_graph_file)
shortest_path_dict = shortest_path_xy(graph)
path_coords = shortest_path_dict.get("path_coords")
start_coord = shortest_path_dict.get("start_coord")
goal_coord = shortest_path_dict.get("goal_coord")
else:
error_exit("No enumerated state graph available to draw solution path")
# Input handling
input_handler = Inputs()
# Main Loop
main = True
while main:
input_handler.onLoop()
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
main = False
if event.type == pygame.KEYDOWN:
if event.key == ord('q'):
pygame.quit()
main = False
sys.exit()
elif event.key == ord('r'):
player_model.reset()
collected_bonus_tile_coords_dict = {}
platform_sprites = get_sprites(level_obj.get_platform_coords(), 'block_tile.png')
input_handler.onEvent(event)
if not main:
break
world.fill(BACKGROUND_COLOR)
camera.update(player_view) # set camera to track player
# Update Player model and view
player_model.update(action=input_handler.getAction(),
precomputed_graph=state_graph, edge_actions_dict=edge_actions_dict)
player_view.update(player_model.state.x, player_model.state.y,
player_model.half_player_w, player_model.half_player_h)
# Update the current score
hit_bonus_coord = player_model.get_hit_bonus_coord()
if hit_bonus_coord is not '':
hit_bonus_coord_x = player_model.state.x // TILE_DIM
hit_bonus_coord_y = player_model.state.y // TILE_DIM - 1
if hit_bonus_coord == 'N':
pass
elif hit_bonus_coord == 'NE':
hit_bonus_coord_x += 1
elif hit_bonus_coord == 'NW':
hit_bonus_coord_x -= 1
else:
error_exit("unrecognized hit bonus coord")
hit_bonus_coord_xy = (hit_bonus_coord_x * TILE_DIM, hit_bonus_coord_y * TILE_DIM)
if hit_bonus_coord_xy not in level_obj.get_bonus_coords():
error_exit("hit bonus tile that is not there: " + str(hit_bonus_coord_xy))
if collected_bonus_tile_coords_dict.get(hit_bonus_coord_xy) is None:
collected_bonus_tile_coords_dict[hit_bonus_coord_xy] = 1
platform_sprites.add(Tile(hit_bonus_coord_xy[0], hit_bonus_coord_xy[1], 'block_tile.png'))
score = len(collected_bonus_tile_coords_dict) * 10
# Draw sprites
entities_to_draw = []
entities_to_draw += list(bonus_sprites) # draw bonus tiles
entities_to_draw += list(platform_sprites) # draw platforms tiles
entities_to_draw += list(one_way_platform_sprites) # draw one-way platform tiles
entities_to_draw += list(hazard_sprites)
entities_to_draw += list(wall_sprites)
entities_to_draw += list(player_list) # draw player
entities_to_draw += list(goal_sprites) # draw goal tiles
for e in entities_to_draw:
world.blit(e.image, camera.apply(e))
# Draw metatile labels
if draw_all_labels or draw_dup_labels:
for coord in level_obj.get_all_possible_coords(): # draw metatile border outlines
tile_rect = pygame.Rect(coord[0], coord[1], TILE_DIM, TILE_DIM)
tile_rect = camera.apply_to_rect(tile_rect) # adjust based on camera
pygame.draw.rect(world, font_color, tile_rect, 1)
for label in metatile_labels: # draw metatile labels
surface, label_x, label_y = label
label_x, label_y = camera.apply_to_coord((label_x, label_y))
world.blit(surface, (label_x + label_padding[0], label_y + label_padding[1]))
# Draw level solution path
if draw_path:
for coord in path_coords:
if coord == start_coord:
color = start_font_color
elif coord == goal_coord:
color = goal_font_color
else:
color = path_font_color
coord = eval(coord)
path_component = pygame.Rect(coord[0], coord[1], 2, 2)
path_component = camera.apply_to_rect(path_component)
pygame.draw.rect(world, color, path_component, 1)
# Draw text labels
label_rect_pairs = []
if player_model.goal_reached():
score += 50
labels = [
("You Win!", 50, COLORS.get('GREEN')),
("Score: %d" % score, 30, COLORS.get('YELLOW')),
("Press 'R' to replay or 'Q' to quit", 30, COLORS.get('YELLOW'))
]
label_rect_pairs = get_label_rect_pairs(center_x=WORLD_X/2, labels=labels)
elif player_model.is_dead():
labels = [
("Game Over", 50, COLORS.get('RED')),
("Score: %d" % score, 30, COLORS.get('YELLOW')),
("Press 'R' to replay or 'Q' to quit", 30, COLORS.get('YELLOW'))
]
label_rect_pairs = get_label_rect_pairs(center_x=WORLD_X / 2, labels=labels)
elif show_score:
labels = [("Score: %d" % score, 50, COLORS.get('YELLOW'))]
label_rect_pairs = get_label_rect_pairs(center_x=WORLD_X / 2, labels=labels)
for label, label_rect in label_rect_pairs:
world.blit(label, label_rect)
pygame.display.flip()
clock.tick(FPS)
else:
# Use random drawn datasets with at least one success for
# categories that few sufficient successful experiments for training
run_non_meta_model(base_model,
common_params,
model_params,
category,
success=True)
# Gradient Boosting
base_model = GradientBoosting
model_params = gradientboosting_params
for category in categories:
if '4_ii' not in category and '5_ii' not in category:
# Use regular random drawn datasets for categories
# that have sufficient successful experiments for training
run_non_meta_model(base_model, common_params, model_params,
category)
else:
# Use random drawn datasets with at least one success for
# categories that few sufficient successful experiments for training
run_non_meta_model(base_model,
common_params,
model_params,
category,
success=True)
# Use cv_stats.pkl to plot all graphs
cv_stats = read_pickle(common_params['stats_path'])
plot_all_graphs(cv_stats)
def main(trial, levels, num_sol, asp, state_graph):
if not (asp or state_graph):
utils.error_exit(
"Must specify at least one validation test to run: --asp or --state_graph"
)
# Get file formats
config_formats = TRIAL_CONFIG_FORMATS.get(trial)
if config_formats is None:
utils.error_exit("--trial must be one of %s" %
str(list(TRIAL_CONFIG_FORMATS.keys())))
prolog_file_format = "level_saved_files_block/prolog_files/%s.pl"
model_str_file_format = "level_saved_files_block/generated_level_model_strs/%s.txt"
assignments_dict_file_format = "level_saved_files_block/generated_level_assignments_dicts/%s.pickle"
# Initialize validation counts
asp_checked_count = 0
asp_valid_count = 0
state_graph_checked_count = 0
state_graph_valid_count = 0
# Validate each solver run
for level in levels:
for config_file_format in config_formats:
for sol in range(num_sol):
prolog_file = prolog_file_format % level
prolog_filename = utils.get_basepath_filename(
prolog_file, 'pl')
config_file = config_file_format % level
config_filename = utils.get_basepath_filename(
config_file, 'json')
answer_set_filename = '_'.join(
[prolog_filename, config_filename,
'a%d' % sol])
if asp:
# Determine ASP checks to perform based on config file contents
config_file_contents = utils.read_json(config_file)
config = config_file_contents['config']
require_all_platforms_reachable = True
require_all_bonus_tiles_reachable = True
if config.get(
'require_all_platforms_reachable') is not None:
require_all_platforms_reachable = eval(
config['require_all_platforms_reachable'])
if config.get(
'require_all_bonus_tiles_reachable') is not None:
require_all_bonus_tiles_reachable = eval(
config['require_all_bonus_tiles_reachable'])
prolog_file_info = get_prolog_file_info(prolog_file)
tile_ids = get_tile_ids_dictionary(prolog_file_info)
model_str_file = model_str_file_format % answer_set_filename
if os.path.exists(model_str_file):
model_str = utils.read_txt(model_str_file)
asp_valid = Solver.asp_is_valid(
check_path=True,
check_onground=require_all_platforms_reachable,
check_bonus=require_all_bonus_tiles_reachable,
model_str=model_str,
player_img='block',
answer_set_filename=answer_set_filename,
tile_ids=tile_ids,
save=False)
status = "ASP VALID" if asp_valid else "ASP INVALID"
print("%s: %s" % (answer_set_filename, status))
asp_checked_count += 1
asp_valid_count += 1 if asp_valid else 0
if state_graph:
assignments_dict_file = assignments_dict_file_format % answer_set_filename
if os.path.exists(assignments_dict_file):
assignments_dict = utils.read_pickle(
assignments_dict_file)
valid_path = Solver.get_state_graph_valid_path(
assignments_dict=assignments_dict,
player_img='block',
prolog_filename=prolog_filename,
answer_set_filename=answer_set_filename,
save=True)
status = "GRAPH VALID" if valid_path else "GRAPH INVALID"
print("%s: %s" % (answer_set_filename, status))
state_graph_checked_count += 1
state_graph_valid_count += 1 if valid_path is not None else 0
# Print validation results summary
if asp:
print("ASPs Checked: %d" % asp_checked_count)
print("ASPs Valid: %d" % asp_valid_count)
if state_graph:
print("State Graphs Checked: %d" % state_graph_checked_count)
print("State Graphs Valid: %d" % state_graph_valid_count)
import utils
# import lfw_dataset
# import numpy as np
# data, attr = lfw_dataset.load_lfw_dataset(dimx = 36, dimy = 36)
# data = np.float32(data) / 255.
# utils.save_pickle(data, "data.pickle")
# utils.save_pickle(data, "attr.pickle")
data = utils.read_pickle("data.pickle")
attr = utils.read_pickle("attr.pickle")
IMG_SHAPE = data.shape[1:]
CODE_SIZE = 256
Xsamp = Xsamp[:, vertices]
ysamp = ysingle[vertices, :]
Xout, yout = prune_disconnected(Xsamp, ysamp)
utils.persist_sparse_data(folder, Xout, yout[:, 32:])
return Xout, yout[:, 32:]
def prune_disconnected(X, y):
keep = np.where(X.sum(axis=1) > 0)[0]
Xkeep = X[keep, :]
Xkeep = Xkeep[:, keep]
ykeep = y[keep, :]
return Xkeep, ykeep
if __name__ == '__main__':
X = utils.read_pickle('../../local_resources/blogcatalog/X.p')
y = utils.read_pickle('../../local_resources/blogcatalog/y.p')
xpath = '../../local_resources/blogcatalog_121_sample/X.p'
ypath = '../../local_resources/blogcatalog_121_sample/y.p'
folder = '../../local_resources/blogcatalog_121_sample'
Xsamp, ysamp = sample_graph(X, y, folder)
print X.sum()
print 'number of vertices connected to one or more other vertices: ', sum(
Xsamp.sum(axis=1) > 0)
print 'label distribution: ', ysamp.sum(axis=0)
print Xsamp.sum()
print Xsamp.shape
print ysamp.shape
import utils
# import func
import pickle
import numpy as np
from func import caption_tokens_to_indices
train_img_embeds = utils.read_pickle("train_img_embeds.pickle")
train_img_fns = utils.read_pickle("train_img_fns.pickle")
val_img_embeds = utils.read_pickle("val_img_embeds.pickle")
val_img_fns = utils.read_pickle("val_img_fns.pickle")
train_captions = utils.read_pickle("train_captions.pickle")
val_captions = utils.read_pickle("val_captions.pickle")
vocab = utils.read_pickle("vocabs.pickle")
# swap the key value of vocab
vocab_inverse = {value: key for key, value in vocab.items()}
train_captions_index = np.array(
caption_tokens_to_indices(train_captions, vocab))
val_captions_index = np.array(caption_tokens_to_indices(val_captions, vocab))
# train_captions = func.get_captions_for_fns(train_img_fns, "captions_train-val2014.zip",
# "annotations/captions_train2014.json")
# val_captions = func.get_captions_for_fns(val_img_fns, "captions_train-val2014.zip",
# "annotations/captions_val2014.json")
# with open("val_captions.pickle", "wb") as fn:
# pickle.dump(val_captions, fn)
def run(seed):
# create folders for scores models and preds
folder_models = './models/age/scores/'
if not os.path.exists(folder_models):
os.makedirs(folder_models)
folder_preds = './predicts/age/scores/'
if not os.path.exists(folder_preds):
os.makedirs(folder_preds)
print('Loading data...')
# load biases
ic_bias = read_pickle('./data/biases/ic_biases.pickle')
ic_bias_site = read_pickle('./data/biases/ic_biases_site.pickle')
fnc_bias = read_pickle('./data/biases/fnc_biases.pickle')
fnc_bias_site = read_pickle('./data/biases/fnc_biases_site.pickle')
pca_bias = read_pickle('./data/biases/200pca_biases.pickle')
pca_bias_site = read_pickle('./data/biases/200pca_biases_site.pickle')
# load classifier and add extra sites2
extra_site = pd.DataFrame()
extra_site['Id'] = np.load('./predicts/classifier/site2_test_new_9735.npy')
# load competiton data
ids_df = pd.read_csv('./data/raw/reveal_ID_site2.csv')
fnc_df = pd.read_csv('./data/raw/fnc.csv')
loading_df = pd.read_csv('./data/raw/loading.csv')
labels_df = pd.read_csv('./data/raw/train_scores.csv')
ids_df = ids_df.append(extra_site)
print('Detected Site2 ids count: ', ids_df['Id'].nunique())
# load created features
agg_df = pd.read_csv('./data/features/agg_feats.csv')
im_df = pd.read_csv('./data/features/im_feats.csv')
dl_df = pd.read_csv('./data/features/dl_feats.csv')
pca_df = pd.read_csv('./data/features/200pca_feats/200pca_3d_k0.csv')
for i in range(1, 6):
part = pd.read_csv(
'./data/features/200pca_feats/200pca_3d_k{}.csv'.format(i))
del part['Id']
pca_df = pd.concat((pca_df, part), axis=1)
# merge data
ic_cols = list(loading_df.columns[1:])
fnc_cols = list(fnc_df.columns[1:])
agg_cols = list(agg_df.columns[1:])
im_cols = list(im_df.columns[1:])
pca_cols = list(pca_df.columns[1:])
dl_cols = list(dl_df.columns[1:])
df = fnc_df.merge(loading_df, on='Id')
df = df.merge(agg_df, how='left', on='Id')
df = df.merge(im_df, how='left', on='Id')
df = df.merge(pca_df, how='left', on='Id')
df = df.merge(dl_df, how='left', on='Id')
df = df.merge(labels_df, how='left', on='Id')
del loading_df, fnc_df, agg_df, im_df, pca_df
gc.collect()
# split train and test
df.loc[df['Id'].isin(labels_df['Id']), 'is_test'] = 0
df.loc[~df['Id'].isin(labels_df['Id']), 'is_test'] = 1
train = df.query('is_test==0')
del train['is_test']
test = df.query('is_test==1')
del test['is_test']
y = train['age'].copy().reset_index(drop=True)
# apply biases
for c in ic_bias_site.keys():
test.loc[~test['Id'].isin(ids_df['Id']), c] += ic_bias[c]
test.loc[test['Id'].isin(ids_df['Id']), c] += ic_bias_site[c]
for c in fnc_bias_site.keys():
test.loc[~test['Id'].isin(ids_df['Id']), c] += fnc_bias[c]
test.loc[test['Id'].isin(ids_df['Id']), c] += fnc_bias_site[c]
for c in pca_bias_site.keys():
test.loc[~test['Id'].isin(ids_df['Id']), c] += pca_bias[c]
test.loc[test['Id'].isin(ids_df['Id']), c] += pca_bias_site[c]
# save df for scaling
df_scale = pd.concat([train, test], axis=0)
# I. Create fnc score
print('Creating FNC score...')
# prepare datasets for fnc score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, fnc_cols)
# define models
names = ['RGF', 'ENet', 'BRidge', 'Huber', 'OMP']
names = [name + '_fnc_seed{}'.format(seed) for name in names]
pack = [
RGFRegressor(max_leaf=1000, reg_depth=5, normalize=True),
ElasticNet(alpha=0.05, l1_ratio=0.5, random_state=0),
BayesianRidge(),
HuberRegressor(epsilon=2.5, alpha=1),
OrthogonalMatchingPursuit(n_nonzero_coefs=300)
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 5, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 5, names)
# save oof, pred, models
np.save(folder_preds + 'fnc_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'fnc_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# II. Create agg score
print('Creating AGG score...')
# prepare datasets for agg score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, agg_cols)
# define models
names = ['RGF', 'ENet', 'Huber']
names = [name + '_agg_seed{}'.format(seed) for name in names]
pack = [
RGFRegressor(max_leaf=1000,
reg_depth=5,
min_samples_leaf=100,
normalize=True),
ElasticNet(alpha=0.05, l1_ratio=0.3, random_state=0),
HuberRegressor(epsilon=2.5, alpha=1)
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 3, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 3, names)
# save oof, pred, models
np.save(folder_preds + 'agg_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'agg_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# III. Create pca score
print('Creating PCA score...')
# prepare datasets for pca score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, pca_cols)
# define models
names = ['RGF', 'ENet', 'BRidge', 'OMP']
names = [name + '_pca_seed{}'.format(seed) for name in names]
pack = [
RGFRegressor(max_leaf=1000,
reg_depth=5,
min_samples_leaf=100,
normalize=True),
ElasticNet(alpha=0.2, l1_ratio=0.2, random_state=0),
BayesianRidge(),
OrthogonalMatchingPursuit()
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 4, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 4, names)
# save oof, pred, models
np.save(folder_preds + 'pca_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'pca_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# IV. Create im score
print('Creating IM score...')
# prepare datasets for pca score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, im_cols)
# define models
names = ['RGF', 'ENet', 'BRidge', 'OMP']
names = [name + '_im_seed{}'.format(seed) for name in names]
pack = [
RGFRegressor(max_leaf=1000,
reg_depth=5,
min_samples_leaf=100,
normalize=True),
ElasticNet(alpha=0.2, l1_ratio=0.2, random_state=0),
BayesianRidge(),
OrthogonalMatchingPursuit()
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 4, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 4, names)
# save oof, pred, models
np.save(folder_preds + 'im_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'im_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# V. Create dl score
print('Creating DL score...')
# prepare datasets for pca score
train_for_score, test_for_score = scale_select_data(
train, test, df_scale, dl_cols)
# define models
names = ['RGF', 'ENet', 'BRidge']
names = [name + '_dl_seed{}'.format(seed) for name in names]
pack = [
RGFRegressor(max_leaf=1000,
reg_depth=5,
min_samples_leaf=100,
normalize=True),
ElasticNet(alpha=0.2, l1_ratio=0.2, random_state=0),
BayesianRidge()
]
# train models
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_for_score] * 3, y)
score_blend = zoo.blend_oof()
pred = zoo.predict([test_for_score] * 3, names)
# save oof, pred, models
np.save(folder_preds + 'dl_score_seed{}.npy'.format(seed), score_blend)
np.save(folder_preds + 'dl_score_test_seed{}.npy'.format(seed), pred)
zoo.save_models(names, folder=folder_models)
# VI. Training and predicting procedure
print('Training has started...')
print('Reading scores from ', folder_preds)
# add scores
for prefix in ['fnc', 'agg', 'im', 'pca', 'dl']:
train[prefix +
'_score'] = np.load(folder_preds +
'{}_score_seed{}.npy'.format(prefix, seed))
test[prefix + '_score'] = np.load(
folder_preds + '{}_score_test_seed{}.npy'.format(prefix, seed))
score_cols = [c for c in train.columns if c.endswith('_score')]
# save df for scaling
df_scale = pd.concat([train, test], axis=0)
# create differents datasets
# linear
linear_cols = sorted(
list(
set(ic_cols + fnc_cols + pca_cols + agg_cols + im_cols) -
set(['IC_20'])))
train_linear, test_linear = scale_select_data(train, test, df_scale,
linear_cols)
# kernel
kernel_cols = sorted(list(set(ic_cols + pca_cols) - set(['IC_20'])))
train_kernel, test_kernel = scale_select_data(train=train,
test=test,
df_scale=df_scale,
cols=kernel_cols,
scale_cols=pca_cols)
# score
sc_cols = sorted(list(set(ic_cols + score_cols) - set(['IC_20'])))
train_sc, test_sc = scale_select_data(train, test, df_scale, sc_cols)
# dl
dict_cols = sorted(
list(
set(ic_cols + fnc_cols + dl_cols + im_cols + agg_cols) -
set(['IC_20'])))
train_dl, test_dl = scale_select_data(train, test, df_scale, dict_cols)
# learning process on different datasets
names = ['MLP', 'RGF', 'SVM', 'BR', 'OMP', 'EN', 'KR']
names = [name + '_seed{}'.format(seed) for name in names]
pack = [
MLPRegressor(activation='tanh', random_state=0),
RGFRegressor(max_leaf=1500, loss='Abs'),
NuSVR(C=10, nu=0.4, kernel='rbf'),
BayesianRidge(),
OrthogonalMatchingPursuitCV(),
ElasticNet(alpha=0.5, l1_ratio=0.7, random_state=0),
KernelRidge(kernel='poly', alpha=0.5)
]
zoo = TrendsModelSklearn(pack, seed=seed)
zoo.fit([train_sc] * 2 + [train_kernel] + [train_linear] * 2 +
[train_dl] * 2, y)
de_blend = zoo.blend_oof()
preds = zoo.predict([test_sc] * 2 + [test_kernel] + [test_linear] * 2 +
[test_dl] * 2,
names,
is_blend=False)
# rewrite folders for models and preds
folder_models = './models/age/stack/'
if not os.path.exists(folder_models):
os.makedirs(folder_models)
folder_preds = './predicts/age/stack/'
if not os.path.exists(folder_preds):
os.makedirs(folder_preds)
print('Saving models to', folder_models)
print('Saving predictions to', folder_preds)
# save oofs and models
zoo.save_oofs(names, folder=folder_preds)
zoo.save_models(names, folder=folder_models)
# stacking predictions
print('Stacking predictions...')
folds = KFold(n_splits=10, shuffle=True, random_state=0)
stack = pd.DataFrame(zoo.oof_preds).T
stack.columns = names
model_stacker_rgf = RGFRegressor(max_leaf=1000,
reg_depth=25,
verbose=False)
rgf_pred = cross_val_predict(model_stacker_rgf,
stack,
y.dropna(),
cv=folds,
n_jobs=-1)
model_stacker_br = BayesianRidge()
br_pred = cross_val_predict(model_stacker_br,
stack,
y.dropna(),
cv=folds,
n_jobs=-1)
model_stacker_rgf.fit(stack, y.dropna())
model_stacker_br.fit(stack, y.dropna())
# save models
save_pickle(model_stacker_br,
folder_models + 'BRidge_stack_seed{}'.format(seed))
save_pickle(model_stacker_rgf,
folder_models + 'RGF_stack_seed{}'.format(seed))
print('Final age NMAE: {:.5f}'.format(
NMAE(y, 0.75 * br_pred + 0.25 * rgf_pred)))
test_preds = pd.DataFrame(preds).T
test_preds.columns = names
age_prediction = pd.DataFrame()
age_prediction['Id'] = test['Id'].values
age_prediction['pred'] = 0.25 * model_stacker_rgf.predict(
test_preds) + 0.75 * model_stacker_br.predict(test_preds)
age_prediction.to_csv(folder_preds + 'age_stack_seed{}.csv'.format(seed),
index=False)
print('age seed pred is saved as',
folder_preds + 'age_stack_seed{}.csv'.format(seed))
def batch_size_scenario():
"""
Generate embeddings using different batch sizes for the ~1000 vertex polblogs network
:return:
"""
import visualisation
s = datetime.datetime.now()
y_path = '../../local_resources/political_blogs/y.p'
x_path = '../../local_resources/political_blogs/X.p'
y = utils.read_pickle(y_path)
log_path = '../../local_resources/tf_logs/polblogs/'
walk_path = '../../local_resources/political_blogs/walks_n1_l10.csv'
size = 2 # dimensionality of the embedding
batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128]
embeddings = []
for batch_size in batch_sizes:
params = Params(walk_path,
batch_size=batch_size,
embedding_size=size,
neg_samples=5,
skip_window=5,
num_pairs=1500,
statistics_interval=10.0,
initial_learning_rate=0.1,
save_path=log_path,
epochs=5,
concurrent_steps=4)
path = '../../local_resources/political_blogs/embeddings/Win_batch_{}_{}.csv'.format(
batch_size, utils.get_timestamp())
embedding_in, embedding_out = HCE.main(params)
visualisation.plot_poincare_embedding(
embedding_in, y,
'../../results/political_blogs/figs/poincare_polar_Win_batch_{}_{}.pdf'
.format(batch_size, utils.get_timestamp()))
visualisation.plot_poincare_embedding(
embedding_out, y,
'../../results/political_blogs/figs/poincare_polar_Wout_batch_{}_{}.pdf'
.format(batch_size, utils.get_timestamp()))
df_in = pd.DataFrame(data=embedding_in,
index=np.arange(embedding_in.shape[0]))
df_in.to_csv(path, sep=',')
df_out = pd.DataFrame(data=embedding_out,
index=np.arange(embedding_out.shape[0]))
df_out.to_csv(
'../../local_resources/political_blogs/embeddings/Wout_batch_{}_{}.csv'
.format(batch_size, utils.get_timestamp()),
sep=',')
print('political blogs embedding generated in: ',
datetime.datetime.now() - s)
embeddings.append(embedding_in)
x, y = utils.read_data(x_path, y_path, threshold=0)
names = [[str(batch_size)] for batch_size in batch_sizes]
n_folds = 10
results = run_detectors.run_all_datasets(embeddings, y, names, classifiers,
n_folds)
all_results = utils.merge_results(results, n_folds)
results, tests = utils.stats_test(all_results)
tests[0].to_csv('../../results/political_blogs/batch_size_pvalues' +
utils.get_timestamp() + '.csv')
tests[1].to_csv('../../results/political_blogs/batch_size_pvalues' +
utils.get_timestamp() + '.csv')
print('macro', results[0])
print('micro', results[1])
macro_path = '../../results/political_blogs/batch_size_macro' + utils.get_timestamp(
) + '.csv'
micro_path = '../../results/political_blogs/batch_size_micro' + utils.get_timestamp(
) + '.csv'
results[0].to_csv(macro_path, index=True)
results[1].to_csv(micro_path, index=True)
return path
def train(args):
#WSGAN related params
lambda_gp = 10
n_critic = 5
hyparam_list = [
("model", args.model_name),
("cube", args.cube_len),
("bs", args.batch_size),
("g_lr", args.g_lr),
("d_lr", args.d_lr),
("z", args.z_dis),
("bias", args.bias),
]
hyparam_dict = OrderedDict(((arg, value) for arg, value in hyparam_list))
log_param = make_hyparam_string(hyparam_dict)
print(log_param)
#define different paths
pickle_path = "." + args.pickle_dir + log_param
image_path = args.output_dir + args.image_dir + log_param
pickle_save_path = args.output_dir + args.pickle_dir + log_param
N = None # None for the whole dataset
VOL_SIZE = 64
train_path = pathlib.Path("../Vert_dataset")
dataset = VertDataset(train_path,
n=N,
transform=transforms.Compose(
[ResizeTo(VOL_SIZE),
transforms.ToTensor()]))
print('Number of samples: ', len(dataset))
dset_loaders = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0)
print('Number of batches: ', len(dset_loaders))
# Build the model
D = _D(args)
G = _G(args)
#Create the solvers
D_solver = optim.Adam(D.parameters(), lr=args.d_lr, betas=args.beta)
G_solver = optim.Adam(G.parameters(), lr=args.g_lr, betas=args.beta)
if torch.cuda.device_count() > 1:
D = nn.DataParallel(D)
G = nn.DataParallel(G)
print("Using {} GPUs".format(torch.cuda.device_count()))
D.cuda()
G.cuda()
elif torch.cuda.is_available():
print("using cuda")
D.cuda()
G.cuda()
#Load checkpoint if available
read_pickle(pickle_path, G, G_solver, D, D_solver)
G_losses = []
D_losses = []
for epoch in range(args.n_epochs):
epoch_start_time = time.time()
print("epoch %d started" % (epoch))
for i, X in enumerate(dset_loaders):
#print(X.shape)
X = X.view(-1, args.cube_len * args.cube_len * args.cube_len)
X = var_or_cuda(X)
X = X.type(torch.cuda.FloatTensor)
Z = generateZ(num_samples=X.size(0), z_size=args.z_size)
#Train the critic
d_loss, Wasserstein_D, gp = train_critic(X, Z, D, G, D_solver,
G_solver)
# Train the generator every n_critic steps
if i % n_critic == 0:
Z = generateZ(num_samples=X.size(0), z_size=args.z_size)
g_loss = train_gen(Z, D, G, D_solver, G_solver)
#Log each iteration
iteration = str(G_solver.state_dict()['state'][
G_solver.state_dict()['param_groups'][0]['params'][0]]['step'])
print('Iter-{}; , D_loss : {:.4}, G_loss : {:.4}, WSdistance : {:.4}, GP : {:.4}'.format(iteration, d_loss.item(), \
g_loss.item(), Wasserstein_D.item(), gp.item() ))
## End of epoch
epoch_end_time = time.time()
#Plot the losses each epoch
G_losses.append(g_loss.item())
D_losses.append(d_loss.item())
plot_losess(G_losses, D_losses, epoch)
if (epoch + 1) % args.image_save_step == 0:
print("Saving voxels")
Z = generateZ(num_samples=8, z_size=args.z_size)
gen_output = G(Z)
samples = gen_output.cpu().data[:8].squeeze().numpy()
samples = samples.reshape(-1, args.cube_len, args.cube_len,
args.cube_len)
Save_Voxels(samples, image_path, iteration)
if (epoch + 1) % args.pickle_step == 0:
print("Pickeling the model")
save_new_pickle(pickle_save_path, iteration, G, G_solver, D,
D_solver)
print("epoch time", (epoch_end_time - epoch_start_time) / 60)
print("epoch %d ended" % (epoch))
print("################################################")
from __future__ import print_function
import numpy as np
import random
# import argparse
# import matplotlib as mpl
# mpl.use('Agg', warn=False)
import matplotlib.pyplot as plt
from nn_mnist_jellyfish import NeuralNetwork
# import mnist
import skl
import utils
imgs1 = skl.get_imgs_by_number(0)
imgs2 = skl.get_imgs_by_number(3)
strength_matrix = utils.read_pickle('pkl/nn_mnist_jellyfish_0.pkl')
# for label, img in imgs1:
propagated_1 = []
propagated_2 = []
r = range(np.min([len(imgs1), len(imgs2)]))
index = random.choice(r)
for i in range(10000):
propagated_1.append(
NeuralNetwork.validate(imgs1[index][1], strength_matrix))
propagated_2.append(
NeuralNetwork.validate(imgs2[index][1], strength_matrix))
propagated_1 = np.array(propagated_1)
propagated_2 = np.array(propagated_2)
fig, axes = plt.subplots(2, 1, figsize=(3, 3))
# Create model
msg_net = nn.Sequential(nn.Linear(4, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.d))
aggr_net = nn.Sequential(nn.Linear(args.d + 1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, 1))
model = ModelDirichlet(aggr_net, msg_net)
model.apply(utils.weights_init)
F = DynamicsFunction(model).to(device)
print("Num. of params: {:d}".format(utils.get_parameters_count(model)))
data = utils.read_pickle(['t', 'x', 'u', 'bcs_dicts'], args.data_path)
dataset = utils.generate_torchgeom_dataset(data)
bcs_dicts = data['bcs_dicts']
if args.batch_size is None:
batch_size = len(dataset)
else:
batch_size = args.batch_size
loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
optimizer = optim.Rprop(F.parameters(), lr=args.lr, step_sizes=(1e-8, 10.))
loss_fn = nn.MSELoss()
# Training
ts = dataset[0].t.shape[0] # assumes the same time grid for all sim-s.
import skl
import utils
import sys
parser = argparse.ArgumentParser()
parser.add_argument('-n', action="store", dest="number")
parser.add_argument('-i',
action="store",
dest="iterations",
default=25000,
help="default: 25000")
args = parser.parse_args()
number = int(args.number)
iterations = int(args.iterations)
best_images = utils.read_pickle('best_images.pkl')
pf = lambda x: (1 / (1 + np.exp(-1 * 10 * x)) - .5) * 1.8 + .05
nn = NeuralNetwork(strength_function=pf, image_scale=8)
img = best_images[number]
print('%s' % number)
start_time = datetime.datetime.now()
for i in range(iterations):
nn.propagate_once(img, gray_max=16)
end_time = datetime.datetime.now()
print('start time:', start_time, 'stop time: ', end_time)
lambda img, matrix: NeuralNetwork.validate_linear(
img, matrix, power=3, weight=100), lambda img, matrix: NeuralNetwork.
validate_threshold(img, matrix, power=3, threshhold=.2, weight=10),
lambda img, matrix: NeuralNetwork.validate_threshold_2(
img, matrix, power=3, weight=100)
]
# print('threshhold %s power %s weight %s' % (threshhold, power, weight))
if num >= 0:
imgs = skl.get_imgs_by_number(num)
else:
imgs = skl.get_imgs_by_number()
# print('test imgs #', size)
strength_matrix_l = [
utils.read_pickle('pkl/nn_growable_' + str(i) + '.pkl') for i in range(10)
]
correct = .0
trails = .0
for i in range(iterations):
label, img = random.choice(imgs)
scores_a = np.array([
validators[0](img, strength_matrix)
for strength_matrix in strength_matrix_l
])
if label == random.choice(np.where(scores_a == scores_a.max())[0]):
correct += 1
if not (i % 1000):
# print(label, scores_a)
pass
args = parser.parse_args()
iterations = int(args.iterations)
num = int(args.num)
'''
threshhold = 0.8
weight = 100
print('threshhold %s weight %s' % (threshhold, weight))
'''
if num >=0:
imgs = skl.get_imgs_by_number(num)
else:
imgs = skl.get_imgs_by_number()
strength_matrix_l = [utils.read_pickle('pkl/nn_meshed_' + str(i) + '.pkl') for i in range(10)]
correct = .0
trails = .0
for i in range(iterations):
trails += 1
label, img = random.choice(imgs)
scores_a = np.array([NeuralNetwork.validate(img, strength_matrix, gray_max=16.) for strength_matrix in strength_matrix_l])
if label == random.choice(np.where(scores_a == scores_a.max())[0]):
correct += 1
if not (i % 10) and i > 0:
# print(round(correct / trails * 100, 2), label, scores_a)
pass
if num >=0:
import pickle
from utils import sample
from utils import read_pickle
from utils import print_sample
import numpy as np
## Generate Names
ix_to_char,char_to_ix = read_pickle('datasets/ix_char_ix.pickle')[0]
def generate(parameters,seed,names = 20):
for name in range(names):
# Sample indices and print them
sampled_indices = sample(parameters, char_to_ix, seed)
print_sample(sampled_indices, ix_to_char)
seed += 1
def main():
print('Program Loaded!')
seed = np.random.randint(100)
sel = input('1 for Boys Names\n2 for Girls Names\n3 for Combined\n>>>')
if sel in ['1','2','3']:
names = int(input('ENTER NUMBER OF NAMES TO BE DISPLAYED\n>>>'))
print('>>>')
if sel == '1':
parameters = read_pickle('datasets/boy_params.txt')[0]
generate(parameters,seed,names)
if sel == '2':
parameters = read_pickle('datasets/girl_params.txt')[0]
generate(parameters,seed,names)
if sel == '3':
parameters = read_pickle('datasets/c_params.txt')[0]
generate(parameters,seed)
device = torch.device(args.device)
msg_net = nn.Sequential(nn.Linear(6, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.d))
aggr_net = nn.Sequential(nn.Linear(args.d + 2, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, args.hs_1), nn.Tanh(),
nn.Linear(args.hs_1, 2))
model = Model(aggr_net, msg_net)
F = DynamicsFunction(model).to(device)
F.load_state_dict(torch.load(args.model_path, map_location=device))
data = utils.read_pickle(['t', 'x', 'u'], args.data_path)
dataset = utils.generate_torchgeom_dataset(data)
loader = DataLoader(dataset, batch_size=1, shuffle=False)
# Loss
loss_fn = nn.MSELoss()
# Testing
diffs_over_time = []
losses = torch.zeros(len(loader))
inds_of_sims_to_show = set([0])
with torch.no_grad():
for i, dp in enumerate(loader):
edge_index = dp.edge_index
import tensorflow as tf
# Initialize the arguments
try:
NUM_EPOCHS = int(sys.argv[1])
NUM_OUTLIERS = int(sys.argv[2])
is_trainable = bool(sys.argv[3])
except:
NUM_EPOCHS = 10
NUM_OUTLIERS = 60 # Estimation of #outliers
is_trainable = True
BATCH_SIZE = 32 # 64
# Read the pickle file
X_train = read_pickle('../audio_data/X_train4d.pkl')
X_test = read_pickle('../audio_data/X_test4d.pkl')
Y_train = read_pickle('../audio_data/Y_train1d.pkl')
print("The shape of X_train/X_test/Y_train: ", X_train.shape, X_test.shape, Y_train.shape)
# Instantiate the model
bigan = BIGAN(X_train.shape[1], X_train.shape[2], X_train.shape[3])
if is_trainable:
# Training the BiGAN
bigan.train_by_batch(X_train, epochs=NUM_EPOCHS, batch_size=BATCH_SIZE)
#bilstm.train_all(X_train_, Y_train_, BATCH_SIZE, NUM_EPOCHS)
else:
# Restore the checkpoint
checkpoint_dir = './runs/checkpoint_bigan'
checkpoint = tf.train.Checkpoint()
import matplotlib.pyplot as plt
import numpy as np
# import random
# import argparse
import matplotlib as mpl
mpl.use('Agg', warn=False)
# import mnist
# import seaborn as sns
# import sys
iters = 1 * 10**4
num = 6
imgs_l = [skl.get_imgs_by_number(i) for i in range(10)]
strength_matrix_l = [
utils.read_pickle('pkl/nn_meshed_' + str(i) + '.pkl') for i in range(10)
]
fig, axes = plt.subplots(1, 1, figsize=(9, 3), sharex=True, sharey=True)
# axes = axes.flatten()
imgs = imgs_l[num]
index = random.choice(range(len(imgs)))
index = 3
print('index of testing img: ', index)
img = imgs[index][1]
# skl.show(img)
# for j, matrix in enumerate(strength_matrix_l):
if True:
results_l = [[], [], [], [], [], [], [], [], [], []]
def train(args):
hyparam_list = [("model", args.model_name), ("cube", args.cube_len),
("bs", args.batch_size), ("g_lr", args.g_lr),
("d_lr", args.d_lr), ("z", args.z_dis),
("bias", args.bias), ("sl", args.soft_label)]
hyparam_dict = OrderedDict(((arg, value) for arg, value in hyparam_list))
log_param = make_hyparam_string(hyparam_dict)
print(log_param)
# for using tensorboard
if args.use_tensorboard:
import tensorflow as tf
summary_writer = tf.summary.FileWriter(args.output_dir + args.log_dir +
log_param)
def inject_summary(summary_writer, tag, value, step):
summary = tf.Summary(
value=[tf.Summary.Value(tag=tag, simple_value=value)])
summary_writer.add_summary(summary, global_step=step)
inject_summary = inject_summary
# datset define
dsets_path = args.input_dir + args.data_dir
print(dsets_path)
dsets = SetDataset(dsets_path, args)
dset_loaders = torch.utils.data.DataLoader(dsets,
batch_size=args.batch_size,
shuffle=True,
num_workers=1)
# model define
D = _D(args)
G = _G(args)
D_solver = optim.Adam(D.parameters(), lr=args.d_lr, betas=args.beta)
G_solver = optim.Adam(G.parameters(), lr=args.g_lr, betas=args.beta)
if args.lrsh:
D_scheduler = MultiStepLR(D_solver, milestones=[500, 1000])
if torch.cuda.is_available():
print("using cuda")
D.cuda()
G.cuda()
criterion = nn.BCELoss()
# pickle_path = args.pickle_dir
pickle_path = 'pickle'
read_pickle(pickle_path, G, G_solver, D, D_solver)
for epoch in range(args.n_epochs):
for i, X in enumerate(dset_loaders):
# print(X.size())
X = var_or_cuda(X)
if X.size()[0] != int(args.batch_size):
# print("batch_size != {} drop last incompatible batch".format(int(args.batch_size)))
continue
Z = generateZ(args)
real_labels = var_or_cuda(torch.ones(args.batch_size))
fake_labels = var_or_cuda(torch.zeros(args.batch_size))
if args.soft_label:
real_labels = var_or_cuda(
torch.Tensor(args.batch_size).uniform_(0.7, 1.2))
fake_labels = var_or_cuda(
torch.Tensor(args.batch_size).uniform_(0, 0.3))
# ============= Train the discriminator =============#
d_real = D(X)
d_real_loss = criterion(d_real, real_labels)
fake = G(Z)
d_fake = D(fake)
d_fake_loss = criterion(d_fake, fake_labels)
d_loss = d_real_loss + d_fake_loss
d_real_acu = torch.ge(d_real.squeeze(), 0.5).float()
d_fake_acu = torch.le(d_fake.squeeze(), 0.5).float()
d_total_acu = torch.mean(torch.cat((d_real_acu, d_fake_acu), 0))
if d_total_acu <= args.d_thresh:
D.zero_grad()
d_loss.backward()
D_solver.step()
# build graph
"""
with summary_writer as w:
w.add_graph(D, X)
w.add_graph(G, Z)
w.close()
exit()
"""
# =============== Train the generator ===============#
Z = generateZ(args)
fake = G(Z)
d_fake = D(fake)
g_loss = criterion(d_fake, real_labels)
D.zero_grad()
G.zero_grad()
g_loss.backward()
G_solver.step()
# ==================== Save Good Results ================#
iteration = str(G_solver.state_dict()['state'][
G_solver.state_dict()['param_groups'][0]['params'][0]]['step'])
if g_loss < 0.7:
samples = fake.cpu().data[:8].squeeze().numpy()
image_path = args.output_dir + args.image_dir + log_param
if not os.path.exists(image_path):
os.makedirs(image_path)
SavePloat_Voxels(samples, image_path, iteration)
# =============== logging each iteration ===============#
if args.use_tensorboard:
log_save_path = args.output_dir + args.log_dir + log_param
if not os.path.exists(log_save_path):
os.makedirs(log_save_path)
info = {
'loss/loss_D(x)': d_real_loss,
'loss/loss_D(G(z))': d_fake_loss,
'loss/loss_D': d_loss,
'loss/loss_G': g_loss,
'loss/acc_D': d_total_acu
}
for tag, value in info.items():
inject_summary(summary_writer, tag, value, iteration)
summary_writer.flush()
# =============== each epoch save model or save image ===============#
print(
'Epoch:{}, Iter-{}; , D_loss : {:.4}, G_loss : {:.4}, D_acu : {:.4}, D_lr : {:.4}'
.format(epoch, iteration, d_loss, g_loss, d_total_acu,
D_solver.state_dict()['param_groups'][0]["lr"]))
if (epoch + 1) % args.image_save_step == 0:
samples = fake.cpu().data[:8].squeeze().numpy()
image_path = args.output_dir + args.image_dir + log_param
if not os.path.exists(image_path):
os.makedirs(image_path)
SavePloat_Voxels(samples, image_path, iteration)
if (epoch + 1) % args.pickle_step == 0:
print('saving pickle')
pickle_save_path = args.output_dir + args.pickle_dir
save_new_pickle(pickle_save_path, iteration, G, G_solver, D,
D_solver)
if args.lrsh:
try:
D_scheduler.step()
except Exception as e:
print("fail lr scheduling", e)
def get_best_params():
cv_output = read_pickle('cv_output.pickle')
best_model_params, top_feat_params, top_model_feat_params, *_ = cv_output
return top_feat_params, top_model_feat_params
import numpy as np
import utils
import seaborn as sns
import matplotlib.pyplot as plt
plt.style.use("seaborn-colorblind")
LOAD_DIR = "results/env_2d"
LOAD_FILE = "1a_medium_norm_lambda.pickle"
LOAD_PATH = os.path.join(LOAD_DIR, LOAD_FILE)
NORMS = [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
LAMBDAS = [0.5, 0.1, 0.05, 0.01, 0.005, 0.001]
RUNS = 20
results = utils.read_pickle(LOAD_PATH)
results_array = np.zeros((len(NORMS), len(LAMBDAS)))
for i, norm in enumerate(NORMS):
for j, lambda_1 in enumerate(LAMBDAS):
accuracies = []
for run_idx in range(RUNS):
key = (norm, lambda_1, run_idx)
if key in results:
accuracies.append(results[key][0])
