def render_video(self, filename):
print('Rendering video {0:s}.mp4'.format(filename))
print('Computing grid embedding')
reducer = umap.UMAP(n_jobs=psutil.cpu_count(logical=True))
grid_embedding = reducer.fit_transform(self.grid)
print('Computing trajectory embedding')
bar = ProgressBar(len(self.trajectory), max_width=40)
for i, t in enumerate(self.trajectory):
states, actions, errors = t
self.compute_trajectory_embedding(reducer, states, actions)
bar.numerator = i
print(bar)
print('Rendering frames')
bar = ProgressBar(len(self.trajectory), max_width=40)
for i, t in enumerate(self.trajectory):
states, actions, errors = t
self.render_frame(i, grid_embedding, self.trajectory_embedding,
errors)
bar.numerator = i
print(bar)
print('Saving file {0}'.format(filename + '.mp4'))
imageio.mimsave(filename + '.mp4', self.images, fps=5)
def test_defined_hour():
progress_bar = ProgressBar(2000)
assert ' 0% ( 0/2,000) [ ] eta --:-- /' == str(progress_bar)
eta._NOW = lambda: 1411868722.0
progress_bar.numerator = 1
assert ' 0% ( 1/2,000) [ ] eta --:-- -' == str(progress_bar)
eta._NOW = lambda: 1411868724.0
progress_bar.numerator = 2
assert ' 0% ( 2/2,000) [ ] eta 1:06:36 \\' == str(progress_bar)
def get_film_reviews(root_url, urls, max_reviews_per_film=None):
allocine_dic = defaultdict(list)
bar = ProgressBar(len(urls), max_width=40)
for i, url in enumerate(urls):
# Log progress
bar.numerator = i + 1
print(bar, end='\r')
sys.stdout.flush()
film_id = re.findall(r'\d+', url)[0]
film_url = "{root}/film/fichefilm-{film_id}/critiques/spectateurs".format(
root=root_url, film_id=film_id)
parse_output = parse_film(film_url, max_reviews_per_film)
if parse_output:
ratings, reviews, dates, helpfuls = parse_output
# Rarely happens
if not (len(ratings) == len(reviews) == len(dates) ==
len(helpfuls)):
print("Error: film-url: " + film_url)
continue
allocine_dic['film-url'].extend(len(ratings) * [film_url])
allocine_dic['rating'].extend(ratings)
allocine_dic['review'].extend(reviews)
allocine_dic['date'].extend(dates)
allocine_dic['helpful'].extend([h[0] for h in helpfuls])
allocine_dic['unhelpful'].extend([h[1] for h in helpfuls])
return allocine_dic
def modelInference(self, testImagesPath = None, outputDir = None, resize = None, validation = None, noiseSet = None, steps = None):
if not validation:
self.modelLoad()
print("\nInferencing on pretrained weights.")
else:
print("Validation about to begin.")
if not noiseSet:
noiseSet = self.noiseSet
if testImagesPath:
self.testImagesPath = testImagesPath
if outputDir:
self.resultDir = outputDir
modelInference = inference(gridSize=self.binnigFactor, inputRootDir=self.testImagesPath, outputRootDir=self.resultDir, modelName=self.modelName, validation=validation)
testImageList = modelInference.testingSetProcessor()
barVal = ProgressBar(len(testImageList) * len(noiseSet), max_width=int(50))
imageCounter = 0
with torch.no_grad():
for noise in noiseSet:
#print(noise)
for imgPath in testImageList:
img = modelInference.inputForInference(imgPath, noiseLevel=noise).to(self.device)
output = self.attentionNet(img)
modelInference.saveModelOutput(output, imgPath, noise, steps)
imageCounter += 1
if imageCounter % 2 == 0:
barVal.numerator = imageCounter
print(Fore.CYAN + "Image Processd |", barVal,Fore.CYAN, end='\r')
print("\n")
def __call__(self):
bar = ProgressBar(len(self.sourceImages), max_width=int(50))
counter = 0
for IC, i in enumerate(self.sourceImages):
#print(i)
tarImgPath = extractFileName(i)
#print(self.targetPath+tarImgPath)
img = cv2.imread(i)
linImg = cv2.imread(self.targetPath+tarImgPath)
#print(img.shape)
imgTemp = img[:img.shape[0]- self.patchSize, : img.shape[1]- self.patchSize]
for i in range(0, imgTemp.shape[0], self.patchSize):
for j in range(0, imgTemp.shape[1], self.patchSize):
patch = img[i:i+ self.patchSize, j:j+ self.patchSize, :]
LinRGB = linImg[i:i+ self.patchSize, j:j+ self.patchSize, :]
sampledLinRGB = quadBayerSampler(LinRGB)
#print (patch.shape)
cv2.imwrite(self.pathGTPatch+str(counter)+".png", patch)
#cv2.imwrite(self.pathGTPatch+str(counter)+"_lRGB.png", LinRGB)
cv2.imwrite(self.pathQBPatch+str(counter)+".png", sampledLinRGB)
counter += 1
if IC % 2 == 0:
bar.numerator = IC
print(Fore.CYAN + "Image Processd |", bar,Fore.CYAN, end='\r')
print ("\n Patch Extracted:", counter)
def eval(model, dataloader):
model.eval()
running_loss = []
total = len(dataloader)
bar = ProgressBar(total, max_width=80)
logger.info("Starting Evaluation")
with torch.no_grad():
for i, data in tqdm(enumerate(dataloader, 0)):
bar.numerator = i + 1
print(bar, end='\r')
inputs = data
outputs = model(inputs)
loss = edit_loss(outputs, inputs)
running_loss.append(loss.item())
sys.stdout.flush()
avg_loss = np.mean(running_loss)
print("Eval Loss: {}".format(avg_loss))
plt.plot(np.linspace(1, total, total).astype(int), running_loss)
if not os.path.exists('losses/'):
os.makedirs('losses/')
plt.savefig('losses/eval_loss_{}.png'.format(args.lr))
def get_film_urls(root_url, max_page=None):
list_url = "{root}/films".format(root=root_url)
r = requests.get(list_url)
soup = BeautifulSoup(r.text, 'html.parser')
pagination = soup.find("div", {"class": "pagination-item-holder"})
pages = pagination.find_all("span")
page_number = int([page.text for page in pages][-1])
if max_page:
if max_page > page_number:
print("Error: max_page is greater than the actual number of pages")
return []
else:
page_number = max_page
out_urls = []
bar = ProgressBar(page_number, max_width=40)
for page_id in range(1, page_number + 1):
# Log progress
bar.numerator = page_id
print(bar, end='\r')
sys.stdout.flush()
# Extend out list with new urls
page_url = "{list_url}/?page={page_num}".format(list_url=list_url,
page_num=page_id)
film_urls = parse_list_page(page_url)
out_urls.extend(film_urls)
return out_urls
def compute_edit_distance(edp: EditDistanceParams,
bitmap_cls: Type[screen.Bitmap],
nominal_colours: Type[colours.NominalColours]):
"""Computes edit distance matrix between all pairs of pixel strings.
Enumerates all possible values of the masked bit representation from
bitmap_cls (assuming it is contiguous, i.e. we enumerate all
2**bitmap_cls.MASKED_BITS values). These are mapped to the dot
representation, turned into coloured pixel strings, and we compute the
edit distance.
The effect of this is that we precompute the effect of storing all possible
byte values against all possible screen backgrounds (e.g. as
influencing/influenced by neighbouring bytes).
"""
bits = bitmap_cls.MASKED_BITS
bitrange = np.uint64(2**bits)
edit = []
for _ in range(len(bitmap_cls.BYTE_MASKS)):
edit.append(
np.zeros(shape=np.uint64(bitrange * bitrange), dtype=np.uint16))
# Matrix is symmetrical with zero diagonal so only need to compute upper
# triangle
bar = ProgressBar((bitrange * (bitrange - 1)) / 2, max_width=80)
num_dots = bitmap_cls.MASKED_DOTS
cnt = 0
for i in range(np.uint64(bitrange)):
for j in range(i):
cnt += 1
if cnt % 10000 == 0:
bar.numerator = cnt
print(bar, end='\r')
sys.stdout.flush()
pair = (np.uint64(i) << bits) + np.uint64(j)
for o, ph in enumerate(bitmap_cls.PHASES):
first_dots = bitmap_cls.to_dots(i, byte_offset=o)
second_dots = bitmap_cls.to_dots(j, byte_offset=o)
first_pixels = pixel_string(
colours.dots_to_nominal_colour_pixel_values(
num_dots, first_dots, nominal_colours, init_phase=ph))
second_pixels = pixel_string(
colours.dots_to_nominal_colour_pixel_values(
num_dots, second_dots, nominal_colours, init_phase=ph))
edit[o][pair] = edit_distance(edp,
first_pixels,
second_pixels,
error=False)
return edit
def plot_m2_model_details(data, path, window=1000):
bar = ProgressBar(data['re'].shape[0], max_width=40)
num_rows = 2
num_cols = 2
for i in range(data['re'].shape[0]):
fig = plt.figure(figsize=(num_cols * 7.00, num_rows * 7.00))
ax = plt.subplot(num_rows, num_cols, 1)
ax.set_xlabel('steps')
ax.set_ylabel('reward')
ax.grid()
t = range(data['re'].shape[1])
mu, sigma = prepare_data(data['re'][i], window)
plot_curve(ax, mu, sigma, t, 'blue')
mu, sigma = prepare_data(data['ri'][i], window)
plot_curve(ax, mu, sigma, t, 'red')
plt.legend(['external reward', 'internal reward'], loc=4)
ax = plt.subplot(num_rows, num_cols, 2)
ax.set_xlabel('steps')
ax.set_ylabel('weight')
ax.grid()
t = range(data['re'].shape[1])
mu, sigma = prepare_data(data['m2w'][i][:, 0], window)
plot_curve(ax, mu, sigma, t, 'blue')
mu, sigma = prepare_data(data['m2w'][i][:, 1], window)
plot_curve(ax, mu, sigma, t, 'red')
plt.legend(['curiosity reward', 'familiarity reward'], loc=4)
ax = plt.subplot(num_rows, num_cols, 3)
ax.set_xlabel('steps')
ax.set_ylabel('error')
ax.set_yscale('log', nonpositive='clip')
ax.grid()
t = range(data['fme'].shape[1])
mu, sigma = prepare_data(data['fme'][i], window)
plot_curve(ax, mu, sigma, t, 'green')
plt.legend(['prediction error'], loc=1)
ax = plt.subplot(num_rows, num_cols, 4)
ax.set_xlabel('reward magnitude')
ax.set_ylabel('log count')
ax.set_yscale('log', nonpositive='clip')
ax.grid()
bins = np.linspace(0, 1, 50)
ax.hist(data['fme'][i], bins, color='darkcyan')
plt.legend(['prediction error reward'], loc=1)
plt.savefig("{0:s}_{1:d}.png".format(path, i))
plt.close()
bar.numerator = i + 1
print(bar)
def load_all_data(self, begin_date, end_date):
con = sqlite3.connect('../data/stock.db')
code_list = con.execute(
"SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_data_list, Y_data_list, DATA_list = [0] * 10, [0] * 10, [0] * 10
idx = 0
split = int(len(code_list) / 9)
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
data = self.load_data(code[0], begin_date, end_date)
data = data.dropna()
X, Y = self.make_x_y(data, code[0])
if len(X) <= 1: continue
code_array = [code[0]] * len(X)
assert len(X) == len(data.loc[29:len(data) - 6, '일자'])
if idx % split == 0:
X_data_list[int(idx / split)] = list(X)
Y_data_list[int(idx / split)] = list(Y)
DATA_list[int(idx / split)] = np.array([
data.loc[29:len(data) - 6, '일자'].values.tolist(),
code_array, data.loc[29:len(data) - 6,
'현재가'], data.loc[34:len(data), '현재가']
]).T.tolist()
else:
X_data_list[int(idx / split)].extend(X)
Y_data_list[int(idx / split)].extend(Y)
DATA_list[int(idx / split)].extend(
np.array([
data.loc[29:len(data) - 6, '일자'].values.tolist(),
code_array, data.loc[29:len(data) - 6, '현재가'],
data.loc[34:len(data), '현재가']
]).T.tolist())
bar.numerator += 1
print("%s | %d" % (bar, len(X_data_list[int(idx / split)])),
end='\r')
sys.stdout.flush()
idx += 1
print("%s" % bar)
print("Merge splited data")
bar = ProgressBar(10, max_width=80)
for i in range(10):
if type(X_data_list[i]) == type(1):
continue
if i == 0:
X_data = X_data_list[i]
Y_data = Y_data_list[i]
DATA = DATA_list[i]
else:
X_data.extend(X_data_list[i])
Y_data.extend(Y_data_list[i])
DATA.extend(DATA_list[i])
bar.numerator = i + 1
print("%s | %d" % (bar, len(DATA)), end='\r')
sys.stdout.flush()
print("%s | %d" % (bar, len(DATA)))
return np.array(X_data), np.array(Y_data), np.array(DATA)
def styleEditor(imList, maskList, bgList, targetRoot, invertedEditor=False):
if invertedEditor == False:
invertedEditorCounter = 1
else:
invertedEditorCounter = 2
imList = imageList(imRoot)
maskList = imageList(maskRoot)
bgList = imageList(bgRoot)
counter = 0
bar = ProgressBar(len(imList) * len(maskList) * len(bgList) *
invertedEditorCounter,
max_width=int(10))
for i in imList:
for m in maskList:
for b in bgList:
mask = cv2.imread(m)
bg = cv2.imread(b)
im = cv2.imread(i)
# Resizing Images
maskResize = cv2.resize(mask, (im.shape[1], im.shape[0]))
bgResize = cv2.resize(bg, (im.shape[1], im.shape[0]))
# Making Mask
mkIverted = 255 - maskResize
# Alpha Blending
blendImage = cv2.bitwise_and(im, mkIverted)
bgBlend = cv2.bitwise_and(bgResize, maskResize)
finalBelnding = cv2.bitwise_or(bgBlend.copy(),
blendImage.copy())
counter += 1
# Exporting Image
cv2.imwrite(targetRoot + "styleImage_" + str(counter) + ".png",
finalBelnding)
if invertedEditor == True:
# Inverted Blending
invertedBelnding = cv2.bitwise_and(im, bgResize)
invertedBelnding = cv2.bitwise_and(invertedBelnding,
mkIverted)
counter += 1
# Exporting Image
cv2.imwrite(
targetRoot + "invertedStyleImage_" + str(counter) +
".png", invertedBelnding)
# Logger
if counter % 2 == 0:
bar.numerator = counter
print(Fore.CYAN + "Processd |", bar, Fore.CYAN, end='\r')
print("\nImage edited {}".format(counter))
def plot_forward_inverse_model_details(data, path, window=1000):
bar = ProgressBar(data['re'].shape[0], max_width=40)
num_rows = 3
num_cols = 2
for i in range(data['re'].shape[0]):
fig = plt.figure(figsize=(num_cols * 7.00, num_rows * 7.00))
ax = plt.subplot(num_rows, num_cols, 1)
ax.set_xlabel('steps')
ax.set_ylabel('reward')
ax.grid()
t = range(data['re'].shape[1])
mu, sigma = prepare_data(data['re'][i], window)
plot_curve(ax, mu, sigma, t, 'blue')
mu, sigma = prepare_data(data['ri'][i], window)
plot_curve(ax, mu, sigma, t, 'red')
plt.legend(['external reward', 'internal reward'], loc=4)
ax = plt.subplot(num_rows, num_cols, 3)
ax.set_xlabel('steps')
ax.set_ylabel('error')
ax.set_yscale('log', nonpositive='clip')
ax.grid()
t = range(data['fme'].shape[1])
mu, sigma = prepare_data(data['fme'][i], window)
plot_curve(ax, mu, sigma, t, 'green')
t = range(data['ime'].shape[1])
mu, sigma = prepare_data(data['ime'][i], window)
plot_curve(ax, mu, sigma, t, 'orchid')
plt.legend(['prediction model error', 'inverse model error'], loc=1)
ax = plt.subplot(num_rows, num_cols, 5)
ax.set_xlabel('reward magnitude')
ax.set_ylabel('log count')
ax.set_yscale('log', nonpositive='clip')
ax.grid()
bins = np.linspace(0, 1, 50)
ax.hist(data['fme'][i], bins, color='darkcyan')
plt.legend(['prediction error reward'], loc=1)
if 'sdm' in data.keys() and 'ldm' in data.keys():
ax = plt.subplot(num_rows, num_cols, 2)
c = ax.pcolormesh(data['sdm'][i], cmap='Reds')
fig.colorbar(c, ax=ax)
ax = plt.subplot(num_rows, num_cols, 4)
c = ax.pcolormesh(data['ldm'][i], cmap='Blues')
fig.colorbar(c, ax=ax)
plt.savefig("{0:s}_{1:d}.png".format(path, i))
plt.close()
bar.numerator = i + 1
print(bar)
def fit(self, dataset, n_epoch=100, epoch_size=5000):
init_op = tf.global_variables_initializer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
test_x, test_y, test_seq_len = dataset.get_test_data()
acc = 0
with tf.Session(config=config) as sess:
#shutil.rmtree('/tmp/TF/MNIST')
self.sw_train = tf.summary.FileWriter('/tmp/TF/MNIST/train',
sess.graph)
self.sw_test = tf.summary.FileWriter('/tmp/TF/MNIST/test')
sess.run(init_op)
for i in range(n_epoch):
print('Epoch %d/%d' % (i + 1, n_epoch))
bar = ProgressBar(int(epoch_size / batch_size), max_width=80)
for j in range(int(epoch_size / batch_size)):
batch_x, batch_y, batch_seq_len = dataset.next_batch(
batch_size)
assert batch_x.shape[0] == batch_size and batch_y.shape[
0] == batch_size and batch_seq_len.shape[
0] == batch_size
summary, _, cost = sess.run(
[self.merged, self.train_op, self.cost],
feed_dict={
self.X: batch_x,
self.Y: batch_y,
self.Seq_len: batch_seq_len,
self.batch_size: batch_size
})
self.sw_train.add_summary(
summary,
i * int(epoch_size / batch_size) + j)
bar.numerator = j + 1
print("%s | loss: %f | test_acc: %.2f" %
(bar, cost, acc * 100),
end='\r')
sys.stdout.flush()
if j % 100 == 0:
summary, cost, acc = sess.run(
[self.merged, self.cost, self.accuracy],
feed_dict={
self.X: test_x,
self.Y: test_y,
self.Seq_len: test_seq_len,
self.batch_size: len(test_x)
})
self.sw_test.add_summary(
summary,
i * int(epoch_size / batch_size) + j)
print()
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
saver = tf.train.Saver()
save_path = saver.save(sess, '%s/model.ckpt' % self.model_dir)
print("Model saved in file: %s" % save_path)
def progress_bar():
denominator = 5 if OPTIONS['--fast'] else 100
bar = ProgressBar(0 if OPTIONS['--undefined'] else denominator)
for i in range(denominator + 1):
bar.numerator = i
print(bar, end='\r')
sys.stdout.flush()
time.sleep(0.25)
bar.force_done = True # Needed in case of --undefined.
print(bar) # Always print one last time.
def progress_bar():
denominator = 5 if OPTIONS['--fast'] else 100
bar = ProgressBar(0 if OPTIONS['--undefined'] else denominator)
for i in range(denominator + 1):
bar.numerator = i
print(bar, end='\r')
sys.stdout.flush()
time.sleep(0.25)
bar.force_done = True # Needed in case of --undefined.
print(bar) # Always print one last time.
def load_all_data(self, begin_date, end_date):
#con = sqlite3.connect('../data/stock.db')
#code_list = con.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
code_list = glob.glob('../data/hdf/*.hdf')
code_list = list(map(lambda x: x.split('.hdf')[0][-6:], code_list))
X_data_list, Y_data_list, DATA_list = [0]*10, [0]*10, [0]*10
idx = 0
split = int(len(code_list) / 9)
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
data = self.load_data(code, begin_date, end_date)
if data is None or len(data) == 0:
continue
data = data.dropna()
len_data = len(data)
X, Y = self.make_x_y(data, code)
if len(X) <= 10: continue
mean_velocity = int(data.loc[len_data-10:len_data,'현재가'].mean()) * int(data.loc[len_data-10:len_data, '거래량'].mean())
#print("mean velocity: %d" % mean_velocity)
if mean_velocity < 1000000000 or mean_velocity < 10000000: # 10억 이하면 pass
continue
code_array = [code] * len(X)
assert len(X) == len(data.loc[29:len(data)-self.predict_dist-1, '일자'])
if idx%split == 0:
X_data_list[int(idx/split)] = list(X)
Y_data_list[int(idx/split)] = list(Y)
DATA_list[int(idx/split)] = np.array([data.loc[29:len(data)-6, '일자'].values.tolist(), code_array, data.loc[29:len(data)-6, '현재가'], data.loc[34:len(data), '현재가'], data.loc[30:len(data)-5, '시가']]).T.tolist()
else:
X_data_list[int(idx/split)].extend(X)
Y_data_list[int(idx/split)].extend(Y)
DATA_list[int(idx/split)].extend(np.array([data.loc[29:len(data)-6, '일자'].values.tolist(), code_array, data.loc[29:len(data)-6, '현재가'], data.loc[34:len(data), '현재가'], data.loc[30:len(data)-5, '시가']]).T.tolist())
bar.numerator += 1
print("%s | %d" % (bar, len(X_data_list[int(idx/split)])), end='\r')
sys.stdout.flush()
idx += 1
print("%s" % bar)
print("Merge splited data")
bar = ProgressBar(10, max_width=80)
for i in range(10):
if type(X_data_list[i]) == type(1):
continue
if i == 0:
X_data = X_data_list[i]
Y_data = Y_data_list[i]
DATA = DATA_list[i]
else:
X_data.extend(X_data_list[i])
Y_data.extend(Y_data_list[i])
DATA.extend(DATA_list[i])
bar.numerator = i+1
print("%s | %d" % (bar, len(DATA)), end='\r')
sys.stdout.flush()
print("%s | %d" % (bar, len(DATA)))
return np.array(X_data), np.array(Y_data), np.array(DATA)
def main():
argc = len(sys.argv) - 1
if (argc == 0): # default
doHelp()
min = 32
max = 255
elif (argc == 2): #user defined
min = int(sys.argv[1])
max = int(sys.argv[2])
else:
doHelp()
sys.exit(0)
# error-fixing
if (min < 0): min = 1
if (max < 0): max = 1
if (min >= 0x110000): min = 0x110000 - 1
if (max >= 0x110000): max = 0x110000 - 1
work = max - min
work_minestone = math.ceil(work / 5) # ceil = floor up
bar = ProgressBar(work, max_width=50)
i = 0
s = ""
for i in range(work + 1):
k = i + min
c = chr(k)
s = s + c
if (k % work_minestone) == 0:
bar.numerator = i
print(bar)
sys.stdout.flush()
bar.numerator = i
print(bar)
print("")
pyperclip.copy(s)
print("done")
def fit(self, X_data, Y_data):
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
batch_size = 64
time_length = 30
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
lr = 0.0005
loss_sum = 0
with tf.Session(config=config) as sess:
sess.run(init_op)
if os.path.exists('%s/model.ckpt.meta' % self.prev_model):
ckpt = tf.train.get_checkpoint_state(self.prev_model)
saver = tf.train.Saver()
saver.restore(sess, ckpt.model_checkpoint_path)
for i in range(self.num_epoch):
lr *= 0.9
print("\nEpoch %d/%d is started" % (i + 1, self.num_epoch),
end='\n')
bar = ProgressBar(len(X_data) / batch_size, max_width=80)
for j in range(int(len(X_data) / batch_size) - 1):
X_batch = X_data[batch_size * j:batch_size *
(j + 1)].reshape(batch_size, time_length,
23)
Y_batch = Y_data[batch_size * j:batch_size * (j + 1)]
_ = sess.run(self.updateModel,
feed_dict={
self.lr: lr,
self.inData: X_batch,
self.target: Y_batch,
self.batch_size: 64,
self.time_length: time_length
})
if j % 10 == 0:
loss = sess.run(self.loss,
feed_dict={
self.lr: lr,
self.inData: X_batch,
self.target: Y_batch,
self.batch_size: 64,
self.time_length: time_length
})
bar.numerator = j + 1
loss_sum = ((j / 10) * loss_sum + loss) / (j / 10 + 1)
print("%s | loss: %f" % (bar, loss_sum), end='\r')
sys.stdout.flush()
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
saver = tf.train.Saver()
save_path = saver.save(sess, '%s/model.ckpt' % self.model_dir)
print("Model saved in file: %s" % save_path)
def plot_vae_forward_model_details(data, path, window=1000):
num_subplots = 4
bar = ProgressBar(data['re'].shape[0], max_width=40)
for i in range(data['re'].shape[0]):
plt.figure(figsize=(8.00, 4 * 5.12))
ax = plt.subplot(num_subplots, 1, 1)
ax.set_xlabel('steps')
ax.set_ylabel('reward')
ax.grid()
t = range(data['re'].shape[1])
mu, sigma = prepare_data(data['re'][i], window)
plot_curve(ax, mu, sigma, t, 'blue')
mu, sigma = prepare_data(data['ri'][i], window)
plot_curve(ax, mu, sigma, t, 'red')
plt.legend(['external reward', 'internal reward'], loc=4)
ax = plt.subplot(num_subplots, 1, 2)
ax.set_xlabel('steps')
ax.set_ylabel('error')
ax.grid()
t = range(data['fme'].shape[1])
mu, sigma = prepare_data(data['fme'][i], window)
plot_curve(ax, mu, sigma, t, 'green')
plt.legend(['prediction error'], loc=1)
ax = plt.subplot(num_subplots, 1, 3)
ax.set_xlabel('steps')
ax.set_ylabel('loss value')
ax.grid()
t = range(data['vl'].shape[1])
mu, sigma = prepare_data(data['vl'][i], window)
plot_curve(ax, mu, sigma, t, 'orchid')
plt.legend(['VAE loss'], loc=1)
ax = plt.subplot(num_subplots, 1, 4)
ax.set_xlabel('reward magnitude')
ax.set_ylabel('log count')
ax.set_yscale('log', nonpositive='clip')
ax.grid()
bins = np.linspace(0, 1, 50)
ax.hist(data['fme'][i], bins, color='darkcyan')
plt.legend(['prediction error reward'], loc=1)
plt.savefig("{0:s}_{1:d}.png".format(path, i))
plt.close()
bar.numerator = i + 1
print(bar)
def load_all_data(self, begin_date, end_date):
code_list = glob.glob('../data/hdf/*.hdf')
code_list = list(map(lambda x: x.split('.hdf')[0][-6:], code_list))
X_data_list, Y_data_list, DATA_list = [0]*10, [0]*10, [0]*10
idx = 0
split = int(len(code_list) / 9)
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
data = self.load_data(code, begin_date, end_date)
if data is None or len(data) == 0:
continue
data = data.dropna()
len_data = len(data)
X, Y = self.make_x_y(data, code)
if len(X) <= 10: continue
mean_velocity = int(data.loc[len_data-10:len_data,'현재가'].mean()) * int(data.loc[len_data-10:len_data, '거래량'].mean())
if mean_velocity < 1000000000: # 10억 이하면 pass
continue
code_array = [code] * len(X)
if len(X) != len(data.loc[self.frame_len-1:len(data)-self.predict_dist-1, '일자']):
print("lenX:%d, lenData:%d"%(len(X), len(data.loc[self.frame_len-1:len(data)-self.predict_dist-1, '일자'])))
if idx%split == 0:
X_data_list[int(idx/split)] = list(X)
Y_data_list[int(idx/split)] = list(Y)
DATA_list[int(idx/split)] = np.array([data.loc[self.frame_len-1:len(data)-(self.predict_dist+1), '일자'].values.tolist(), code_array, data.loc[self.frame_len-1:len(data)-(self.predict_dist+1), '현재가'], data.loc[self.frame_len+self.predict_dist-1:len(data), '현재가'], data.loc[self.frame_len:len(data)-self.predict_dist, '시가']]).T.tolist()
else:
X_data_list[int(idx/split)].extend(X)
Y_data_list[int(idx/split)].extend(Y)
DATA_list[int(idx/split)].extend(np.array([data.loc[self.frame_len-1:len(data)-(self.predict_dist+1), '일자'].values.tolist(), code_array, data.loc[self.frame_len-1:len(data)-(self.predict_dist+1), '현재가'], data.loc[self.frame_len+self.predict_dist-1:len(data), '현재가'], data.loc[self.frame_len:len(data)-self.predict_dist, '시가']]).T.tolist())
bar.numerator += 1
print("%s | %d" % (bar, len(X_data_list[int(idx/split)])), end='\r')
sys.stdout.flush()
idx += 1
print("%s" % bar)
print("Merge splited data")
bar = ProgressBar(10, max_width=80)
for i in range(10):
if type(X_data_list[i]) == type(1):
continue
if i == 0:
X_data = X_data_list[i]
Y_data = Y_data_list[i]
DATA = DATA_list[i]
else:
X_data.extend(X_data_list[i])
Y_data.extend(Y_data_list[i])
DATA.extend(DATA_list[i])
bar.numerator = i+1
print("%s | %d" % (bar, len(DATA)), end='\r')
sys.stdout.flush()
print("%s | %d" % (bar, len(DATA)))
return np.array(X_data), np.array(Y_data), np.array(DATA)
def test_undefined():
misc.terminal_width = lambda: 40
progress_bar = ProgressBar(None, max_width=30)
assert '0 [? ] eta --:-- /' == str(progress_bar)
assert '0 [ ? ] eta --:-- -' == str(progress_bar)
assert '0 [ ? ] eta --:-- \\' == str(progress_bar)
eta._NOW = lambda: 1411868722.0
progress_bar.numerator = 10
assert '10 [ ? ] eta --:-- |' == str(progress_bar)
assert '10 [ ? ] eta --:-- /' == str(progress_bar)
eta._NOW = lambda: 1411868722.5
progress_bar.numerator = 100
assert '100 [ ? ] eta --:-- -' == str(progress_bar)
eta._NOW = lambda: 1411868723.0
progress_bar.numerator = 1954727
assert '1,954,727 [ ? ] eta --:-- \\' == str(progress_bar)
assert '1,954,727 [ ? ] eta --:-- |' == str(progress_bar)
def test_defined():
progress_bar = ProgressBar(2000)
assert ' 0% ( 0/2,000) [ ] eta --:-- /' == str(progress_bar)
assert ' 0% ( 0/2,000) [ ] eta --:-- -' == str(progress_bar)
assert ' 0% ( 0/2,000) [ ] eta --:-- \\' == str(progress_bar)
eta._NOW = lambda: 1411868722.0
progress_bar.numerator = 102
assert ' 5% ( 102/2,000) [ ] eta --:-- |' == str(progress_bar)
assert ' 5% ( 102/2,000) [ ] eta --:-- /' == str(progress_bar)
eta._NOW = lambda: 1411868722.5
progress_bar.numerator = 281
assert ' 14% ( 281/2,000) [ ] eta 00:05 -' == str(progress_bar)
eta._NOW = lambda: 1411868723.0
progress_bar.numerator = 593
assert ' 29% ( 593/2,000) [## ] eta 00:03 \\' == str(progress_bar)
eta._NOW = lambda: 1411868723.5
progress_bar.numerator = 1925
assert ' 96% (1,925/2,000) [###### ] eta 00:01 |' == str(progress_bar)
eta._NOW = lambda: 1411868724.0
progress_bar.numerator = 1999
assert ' 99% (1,999/2,000) [###### ] eta 00:01 /' == str(progress_bar)
eta._NOW = lambda: 1411868724.5
progress_bar.numerator = 2000
assert '100% (2,000/2,000) [#######] eta 00:00 -' == str(progress_bar)
assert '100% (2,000/2,000) [#######] eta 00:00 \\' == str(progress_bar)
assert '100% (2,000/2,000) [#######] eta 00:00 |' == str(progress_bar)
def modelInference(self, testImagesPath = None, outputDir = None, resize = None, validation = None, noiseSet = None, steps = None):
if not validation:
self.modelLoad(cpu=False)
print("\nInferencing on pretrained weights.")
else:
print("Validation about to begin.")
if not noiseSet:
noiseSet = self.noiseSet
if testImagesPath:
self.testImagesPath = testImagesPath
if outputDir:
self.resultDir = outputDir
modelInference = inference(inputRootDir=self.testImagesPath, outputRootDir=self.resultDir, modelName=self.modelName, validation=validation)
testImageList = modelInference.testingSetProcessor()
#print(testImageList, self.testImagesPath)
barVal = ProgressBar(len(testImageList)/3, max_width=int(50))
imageCounter = 0
PSNRval = []
SSIMVal = []
c = 0
from datetime import datetime
with torch.no_grad():
for imgPath in testImageList:
torch.cuda.empty_cache()
if "_medium" in imgPath:
#if int(extractFileName(imgPath, True).split("_")[0]) % 3 ==0:
#print(extractFileName(imgPath, True).split("_")[0])
c += 1
device = self.device
imgLDR, lumLDR = modelInference.inputForInference(imgPath, noiseLevel=0)#.to(self.device)
#print(imgL.shape, imgR.shape, imgPath)
a = datetime.now()
output = self.attentionNet(imgLDR.to(device))#.to(device)
torch.cuda.empty_cache()
output = self.HDRRec(output.detach())
b = datetime.now()
d = b - a
#print( d)
torch.cuda.empty_cache()
modelInference.saveModelOutput(output, imgPath, steps)
imageCounter += 1
if imageCounter % 2 == 0:
barVal.numerator = imageCounter
print(Fore.CYAN + "Image Processd |", barVal,Fore.CYAN, end='\r')
print(c)
def report_progress(value, title='', init=False):
global bar, start_time
if init:
start_time = time.time()
print(datetime.now().strftime('>>> Start @ %Y-%m-%d %H:%M:%S'))
bar = ProgressBar(value, max_width=100)
else:
bar.numerator = value
elapsed_time = time.strftime("%H:%M:%S",
time.gmtime(time.time() - start_time))
sys.stdout.write('\r>>> {0} = {1} {2}'.format(elapsed_time, bar, title))
sys.stdout.flush()
return
def load_all_data(self, begin_date, end_date):
con = sqlite3.connect('../data/stock.db')
code_list = con.execute("SELECT name FROM sqlite_master WHERE type='table'").fetchall()
X_data_list, Y_data_list, DATA_list = [0]*10, [0]*10, [0]*10
idx = 0
split = int(len(code_list) / 9)
bar = ProgressBar(len(code_list), max_width=80)
for code in code_list:
data = self.load_data(code[0], begin_date, end_date)
data = data.dropna()
X, Y = self.make_x_y(data, code[0])
if len(X) <= 1: continue
code_array = [code[0]] * len(X)
assert len(X) == len(data.loc[29:len(data)-6, '일자'])
if idx%split == 0:
X_data_list[int(idx/split)] = list(X)
Y_data_list[int(idx/split)] = list(Y)
DATA_list[int(idx/split)] = np.array([data.loc[29:len(data)-6, '일자'].values.tolist(), code_array, data.loc[29:len(data)-6, '현재가'], data.loc[34:len(data), '현재가']]).T.tolist()
else:
X_data_list[int(idx/split)].extend(X)
Y_data_list[int(idx/split)].extend(Y)
DATA_list[int(idx/split)].extend(np.array([data.loc[29:len(data)-6, '일자'].values.tolist(), code_array, data.loc[29:len(data)-6, '현재가'], data.loc[34:len(data), '현재가']]).T.tolist())
bar.numerator += 1
print("%s | %d" % (bar, len(X_data_list[int(idx/split)])), end='\r')
sys.stdout.flush()
idx += 1
print("%s" % bar)
print("Merge splited data")
bar = ProgressBar(10, max_width=80)
for i in range(10):
if type(X_data_list[i]) == type(1):
continue
if i == 0:
X_data = X_data_list[i]
Y_data = Y_data_list[i]
DATA = DATA_list[i]
else:
X_data.extend(X_data_list[i])
Y_data.extend(Y_data_list[i])
DATA.extend(DATA_list[i])
bar.numerator = i+1
print("%s | %d" % (bar, len(DATA)), end='\r')
sys.stdout.flush()
print("%s | %d" % (bar, len(DATA)))
return np.array(X_data), np.array(Y_data), np.array(DATA)
def main():
parser = ArgumentParser()
parser.add_argument('--debug', action='store_true')
parser.add_argument('--delete',
action='store_true',
help="Delete old mails, no ask.")
args = parser.parse_args()
cfg = load_config()
cfg.debug = args.debug
popfunc = POP3_SSL if cfg.pop3.ssl else POP3
pop = popfunc(cfg.pop3.host)
pop.user(cfg.pop3.user)
pop.pass_(cfg.pop3.passwd)
num = len(pop.list()[1])
n_delete = 0
bar = ProgressBar(num)
for i in range(1, num + 1):
bar.numerator = i - 1
if not cfg.debug:
print(bar, end='\r')
mail = pop.retr(i)[1]
if to_delete(mail, cfg):
n_delete += 1
if cfg.debug:
print("Mark {} to be delete".format(i))
pop.dele(i)
if n_delete == MAX_DELETE:
break
if not args.delete:
answer = input("Okay to delete {} mails? (y/N) ".format(n_delete))
if answer != 'y':
pop.rset()
pop.quit()
if n_delete == MAX_DELETE:
print(
"There may be more mails to delete. You may want to re-run this script."
)
def main():
session = vk.Session(TOKEN)
api = vk.API(session)
user_ids_names = get_dialogs(api)
selected_number = show_prompt(user_ids_names)
id = user_ids_names[selected_number]["id"]
dirname = mkdir(user_ids_names[selected_number]["name"])
msgs = get_msgs(api, id)
urls = get_urls(msgs)
bar = ProgressBar(0, max_width=60)
bar.numerator = 0
with ThreadPoolExecutor(max_workers=5) as executor:
executor.map(lambda url: download_pics(url, dirname, bar), urls)
def write_results(self, entries_to_process):
'''Write results into CSV file'''
counter = 1
# Create progress bar
bar = ProgressBar(entries_to_process, max_width=72)
# Write CSV header
csv_writer = self.open_csv_file()
# Iter through each feed entry from the hpHosts feed
for feed_entry in self.hphosts_feed.entries:
# Stop processing if the number of entries are higher than in '-n'
if counter > entries_to_process:
break
result = {}
# Update progress bar
bar.numerator = counter
print(bar, end='\r')
# Write phishing site details into CSV
result['Phishing Site Domain'] = feed_entry.title
result['Added to hpHosts'] = parse(feed_entry.published)
result['Phishing Site IP Address'] = re.findall(
r'[0-9]+(?:\.[0-9]+){3}', feed_entry.summary)[0]
# Iterate through the third-party DNS services
for resolver_name in self.resolver_names:
try:
dns_resolvers = self.resolvers[resolver_name]['resolvers']
phishing_domain = result['Phishing Site Domain']
resolver = DnsResolver(dns_resolvers)
# Retrieve the IP addresses that the third-party DNS service resolves
ip_addresses = resolver.get_ip_address(phishing_domain)
except Exception as e:
# Write DNS lookup error message in the CSV file
result[resolver_name] = e
else:
blockpages = self.resolvers[resolver_name]['blockpages']
result[resolver_name] = self.generate_result(
ip_addresses, blockpages, resolver_name)
# Write results into file
csv_writer.writerow(result)
# Flush file after writing each line
self.output_file_handler.flush()
counter += 1
# Close output file
self.output_file_handler.close()
return counter
def __call__(self):
bar = ProgressBar(len(self.sourceImages), max_width=int(50))
counter = 0
for IC, i in enumerate(self.sourceImages):
img = cv2.imread(i)
imgTemp = img[:img.shape[0] - self.patchSize, :img.shape[1] -
self.patchSize]
for i in range(0, imgTemp.shape[0], self.patchSize):
for j in range(0, imgTemp.shape[1], self.patchSize):
patch = img[i:i + self.patchSize, j:j + self.patchSize, :]
#print (patch.shape)
cv2.imwrite(self.targetPath + str(counter) + ".png", patch)
counter += 1
if IC % 2 == 0:
bar.numerator = IC
print(Fore.CYAN + "Image Processd |", bar, Fore.CYAN, end='\r')
print("\n Patch Extracted:", counter)
def main():
parser = ArgumentParser()
parser.add_argument('--debug', action='store_true')
parser.add_argument('--delete', action='store_true',
help="Delete old mails, no ask.")
args = parser.parse_args()
cfg = load_config()
cfg.debug = args.debug
popfunc = POP3_SSL if cfg.pop3.ssl else POP3
pop = popfunc(cfg.pop3.host)
pop.user(cfg.pop3.user)
pop.pass_(cfg.pop3.passwd)
num = len(pop.list()[1])
n_delete = 0
bar = ProgressBar(num)
for i in range(1, num+1):
bar.numerator = i - 1
if not cfg.debug:
print(bar, end='\r')
mail = pop.retr(i)[1]
if to_delete(mail, cfg):
n_delete += 1
if cfg.debug:
print("Mark {} to be delete".format(i))
pop.dele(i)
if n_delete == MAX_DELETE:
break
if not args.delete:
answer = input("Okay to delete {} mails? (y/N) ".format(n_delete))
if answer != 'y':
pop.rset()
pop.quit()
if n_delete == MAX_DELETE:
print("There may be more mails to delete. You may want to re-run this script.")
def main():
Windows.enable() # Does nothing if not on Windows.
# Prepare.
if os.name == 'nt':
locale.setlocale(locale.LC_ALL, 'english-us')
else:
locale.resetlocale()
progress_bar = ProgressBar(5 if OPTIONS['--fast'] else 100)
progress_bar.bar.CHAR_FULL = Color('{autoyellow}#{/autoyellow}')
progress_bar.bar.CHAR_LEADING = Color('{autoyellow}#{/autoyellow}')
progress_bar.bar.CHAR_LEFT_BORDER = Color('{autoblue}[{/autoblue}')
progress_bar.bar.CHAR_RIGHT_BORDER = Color('{autoblue}]{/autoblue}')
# Run.
for i in range(6 if OPTIONS['--fast'] else 101):
progress_bar.numerator = i
print(progress_bar, end='\r')
sys.stdout.flush()
time.sleep(0.25)
print(progress_bar) # Always print one last time.
def test_edit_distances_hgr(self):
"""Assert invariants and symmetries of the edit distance matrices."""
for p in PALETTES:
ed = screen.HGRBitmap.edit_distances(p)
print(p)
bar = ProgressBar((4 * 2**14 * (2**14 - 1)) / 2, max_width=80)
cnt = 0
for ph in range(2):
# TODO: for HGR this invariant isn't true, all-0 and all-1
# values for header/footer/body with/without palette bit can
# also have zero difference
# # Only zero entries should be on diagonal, i.e. of form
# # i << 14 + i
# zeros = np.arange(len(ed[ph]))[ed[ph] == 0]
# for z in zeros:
# z1 = z & (2**14-1)
# z2 = (z >> 14) & (2**14-1)
# if z1 != z2:
# self.assertEqual(z1, z2)
# Assert that matrix is symmetrical
for i in range(2**14):
for j in range(i):
cnt += 1
if cnt % 10000 == 0:
bar.numerator = cnt
print(bar, end='\r')
sys.stdout.flush()
self.assertEqual(
ed[ph][(i << 14) + j],
ed[ph][(j << 14) + i],
)
# Matrix is positive definite
self.assertGreaterEqual(ed[ph][(i << 14) + j], 0)
def load_current_data(self):
code_list = glob.glob('../data/hdf/*.hdf')
code_list = list(map(lambda x: x.split('.hdf')[0][-6:], code_list))
X_test = []
DATA = []
first = True
bar = ProgressBar(len(code_list), max_width=80)
#for code in code_list:
code_list_ret = []
for i, code in enumerate(code_list):
bar.numerator = i + 1
print("%s | %d" % (bar, len(X_test)), end='\r')
sys.stdout.flush()
df = pd.read_hdf('../data/hdf/%s.hdf' % code, 'day').sort_index()
data = df.iloc[-self.frame_len:, :]
if pd.to_numeric(data.loc[:, '현재가']).mean() * pd.to_numeric(
data.loc[:, '거래량']).mean() < 1000000000:
continue
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
data.loc[:, 'month'] = data.loc[:, '일자'] % 10000 / 100
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < self.frame_len:
continue
try:
data_t = self.scaler[code].transform(np.array(data))
except (KeyError, ValueError):
continue
DATA.append(int(data.loc[len(data) - 1, '현재가']))
code_list_ret.append(code)
X_test.extend(np.array(data_t))
X_test = np.array(X_test).reshape(-1, 23 * self.frame_len)
print()
assert len(X_test) == len(code_list_ret)
assert len(X_test) == len(DATA)
return X_test, code_list_ret, DATA
def fit(self, X_data, Y_data):
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
batch_size = 64
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
lr = 0.0005
with tf.Session(config=config) as sess:
sess.run(init_op)
for i in range(self.num_epoch):
lr *= 0.9
print("\nEpoch %d/%d is started" % (i + 1, self.num_epoch),
end='\n')
bar = ProgressBar(len(X_data) / batch_size, max_width=80)
for j in range(int(len(X_data) / batch_size) - 1):
X_batch = X_data[batch_size * j:batch_size * (j + 1)]
Y_batch = Y_data[batch_size * j:batch_size * (j + 1)]
_ = sess.run(self.updateModel,
feed_dict={
self.lr: lr,
self.scalarInput: X_batch,
self.target: Y_batch
})
if j % 10 == 0:
loss = sess.run(self.loss,
feed_dict={
self.lr: lr,
self.scalarInput: X_batch,
self.target: Y_batch
})
bar.numerator = j + 1
print("%s | loss: %f" % (bar, loss), end='\r')
sys.stdout.flush()
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
save_path = self.saver.save(sess, '%s/model.ckpt' % self.model_dir)
print("Model saved in file: %s" % save_path)
def load_current_data(self):
code_list = glob.glob('../data/hdf/*.hdf')
code_list = list(map(lambda x: x.split('.hdf')[0][-6:], code_list))
X_test = []
DATA = []
first = True
bar = ProgressBar(len(code_list), max_width=80)
#for code in code_list:
code_list_ret = []
for i, code in enumerate(code_list):
bar.numerator = i+1
print("%s | %d" % (bar, len(X_test)), end='\r')
sys.stdout.flush()
df = pd.read_hdf('../data/hdf/%s.hdf'%code, 'day').sort_index()
data = df.iloc[-self.frame_len:,:]
if pd.to_numeric(data.loc[:, '현재가']).mean() * pd.to_numeric(data.loc[:, '거래량']).mean() < 1000000000:
continue
data = data.reset_index()
for col in data.columns:
try:
data.loc[:, col] = data.loc[:, col].str.replace('--', '-')
data.loc[:, col] = data.loc[:, col].str.replace('+', '')
except AttributeError as e:
pass
data.loc[:, 'month'] = data.loc[:, '일자']%10000/100
data = data.drop(['일자', '체결강도'], axis=1)
if len(data) < self.frame_len:
continue
try:
data_t = self.scaler[code].transform(np.array(data))
except (KeyError, ValueError):
continue
DATA.append(int(data.loc[len(data)-1, '현재가']))
code_list_ret.append(code)
X_test.extend(np.array(data_t))
X_test = np.array(X_test).reshape(-1, 23*self.frame_len)
print()
assert len(X_test) == len(code_list_ret)
assert len(X_test) == len(DATA)
return X_test, code_list_ret, DATA
def fit(self, X_data, Y_data):
# Add an op to initialize the variables.
init_op = tf.global_variables_initializer()
batch_size = 128
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
lr = 0.005
loss_sum = 0
with tf.Session(config=config) as sess:
sess.run(init_op)
if os.path.exists('%s/model.ckpt.meta'):
ckpt = tf.train.get_checkpoint_state(self.model_dir)
saver = tf.train.Saver()
saver.restore(sess, ckpt.model_checkpoint_path)
for i in range(self.num_epoch):
lr *= 0.9
print("\nEpoch %d/%d is started" % (i+1, self.num_epoch), end='\n')
bar = ProgressBar(len(X_data)/batch_size, max_width=80)
for j in range(int(len(X_data)/batch_size)-1):
X_batch = X_data[batch_size*j:batch_size*(j+1)]
Y_batch = Y_data[batch_size*j:batch_size*(j+1)]
_ = sess.run(self.updateModel, feed_dict={self.lr:lr, self.scalarInput: X_batch, self.target: Y_batch})
if j%10 == 0:
loss = sess.run(self.loss, feed_dict={self.lr:lr, self.scalarInput: X_batch, self.target: Y_batch})
bar.numerator = j+1
loss_sum = ((j/10)*loss_sum + loss)/(j/10+1)
print("%s | loss: %f" % (bar, loss_sum), end='\r')
sys.stdout.flush()
if not os.path.exists(self.model_dir):
os.makedirs(self.model_dir)
saver = tf.train.Saver()
save_path = saver.save(sess,'%s/model.ckpt' % self.model_dir)
print("Model saved in file: %s" % save_path)
for i, zipFile in enumerate(process_zips):
archive = zipfile.ZipFile(zipFile, 'r')
file_paths = zipfile.ZipFile.namelist(archive)
bar = ProgressBar(len(file_paths), max_width=40)
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count()-1 or 1)
for k, file_path in enumerate(file_paths):
data = archive.read(file_path)
openfile = file_path[2:] #filename
sponsored = train.loc[train['file'] == openfile]
if not sponsored.empty:
pool.apply_async(parseFile, args = (data, openfile, int(sponsored['sponsored']), ), callback = addNodes)
testing = sample.loc[sample['file'] == openfile]
if not testing.empty:
pool.apply_async(parseFile, args = (data, openfile, 2, ), callback = addNodes)
bar.numerator = k
print("Folder:", i, bar, end='\r')
sys.stdout.flush()
pool.close()
pool.join()
print()
print("Size: ", len(q))
#print("Sponsored pages: ", G.out_degree("SPONSORED"))
#print("Normal pages: ", G.out_degree("NOTSPONSORED"))
#if G.out_degree("TESTING") != 235917:
#print("Error, invalid number of testing nodes.")
sponsoredDict_reordered = defaultdict(list) notsponsoredDict_reordered = defaultdict(list) for website, filenames in sponsoredDict.items(): for f in filenames: sponsoredDict_reordered[f].append(website) for website, filenames in notsponsoredDict.items(): for f in filenames: notsponsoredDict_reordered[f].append(website) bar = ProgressBar(len(train), max_width=40) train_data = [] for index, row in train.iterrows(): website_string = ' '.join(sponsoredDict_reordered[row['file']]) + ' '.join(notsponsoredDict_reordered[row['file']]) bar.numerator = index print(bar, end='\r') sys.stdout.flush() train_data.append(website_string) print() bar = ProgressBar(len(sample), max_width=40) sample_data = [] for index, row in sample.iterrows(): website_string = ' '.join(sampleDict[row['file']]) bar.numerator = index print(bar, end='\r') sys.stdout.flush() sample_data.append(website_string) print()
def test_defined_long():
progress_bar = ProgressBar(20)
assert ' 0% ( 0/20) [ ] eta --:-- -' == str(progress_bar)
assert ' 0% ( 0/20) [ ] eta --:-- \\' == str(progress_bar)
eta._NOW = lambda: 1411868722.0
progress_bar.numerator = 1
assert ' 5% ( 1/20) [ ] eta --:-- |' == str(progress_bar)
assert ' 5% ( 1/20) [ ] eta --:-- /' == str(progress_bar)
eta._NOW = lambda: 1411868722.5
progress_bar.numerator = 2
assert ' 10% ( 2/20) [# ] eta 00:09 -' == str(progress_bar)
eta._NOW = lambda: 1411868723.0
progress_bar.numerator = 3
assert ' 15% ( 3/20) [# ] eta 00:09 \\' == str(progress_bar)
eta._NOW = lambda: 1411868723.5
progress_bar.numerator = 4
assert ' 20% ( 4/20) [## ] eta 00:08 |' == str(progress_bar)
eta._NOW = lambda: 1411868724.0
progress_bar.numerator = 5
assert ' 25% ( 5/20) [### ] eta 00:08 /' == str(progress_bar)
eta._NOW = lambda: 1411868724.5
progress_bar.numerator = 6
assert ' 30% ( 6/20) [### ] eta 00:07 -' == str(progress_bar)
eta._NOW = lambda: 1411868725.0
progress_bar.numerator = 7
assert ' 35% ( 7/20) [#### ] eta 00:07 \\' == str(progress_bar)
eta._NOW = lambda: 1411868725.5
progress_bar.numerator = 8
assert ' 40% ( 8/20) [##### ] eta 00:06 |' == str(progress_bar)
eta._NOW = lambda: 1411868726.0
progress_bar.numerator = 9
assert ' 45% ( 9/20) [##### ] eta 00:06 /' == str(progress_bar)
eta._NOW = lambda: 1411868726.5
progress_bar.numerator = 10
assert ' 50% (10/20) [###### ] eta 00:05 -' == str(progress_bar)
eta._NOW = lambda: 1411868727.0
progress_bar.numerator = 11
assert ' 55% (11/20) [####### ] eta 00:05 \\' == str(progress_bar)
eta._NOW = lambda: 1411868727.5
progress_bar.numerator = 12
assert ' 60% (12/20) [####### ] eta 00:04 |' == str(progress_bar)
eta._NOW = lambda: 1411868728.0
progress_bar.numerator = 13
assert ' 65% (13/20) [######## ] eta 00:04 /' == str(progress_bar)
eta._NOW = lambda: 1411868728.5
progress_bar.numerator = 14
assert ' 70% (14/20) [######### ] eta 00:03 -' == str(progress_bar)
eta._NOW = lambda: 1411868729.0
progress_bar.numerator = 15
assert ' 75% (15/20) [######### ] eta 00:03 \\' == str(progress_bar)
eta._NOW = lambda: 1411868729.5
progress_bar.numerator = 16
assert ' 80% (16/20) [########## ] eta 00:02 |' == str(progress_bar)
eta._NOW = lambda: 1411868730.0
progress_bar.numerator = 17
assert ' 85% (17/20) [########### ] eta 00:02 /' == str(progress_bar)
eta._NOW = lambda: 1411868730.5
progress_bar.numerator = 18
assert ' 90% (18/20) [########### ] eta 00:01 -' == str(progress_bar)
eta._NOW = lambda: 1411868731.0
progress_bar.numerator = 19
assert ' 95% (19/20) [############ ] eta 00:01 \\' == str(progress_bar)
eta._NOW = lambda: 1411868731.5
progress_bar.numerator = 20
assert '100% (20/20) [#############] eta 00:00 |' == str(progress_bar)
test_files = set(pd.read_csv('./data/sampleSubmission_v2.csv').file.values)
filepaths = glob.glob('data/*/*.txt')
#random.shuffle(filepaths)
#filepaths = filepaths[0:1000]
bar = ProgressBar(len(filepaths), max_width=40)
print("--- Started processing")
redis_conn = Redis('192.168.1.140', 6379)
q = Queue("low", connection=redis_conn)
jobs = []
for i, filepath in enumerate(filepaths):
bar.numerator = i
filename = os.path.basename(filepath)
if filename in train_keys:
jobs.append(q.enqueue_call(func=processFile, args=(filepath, train_keys[filename],), timeout=300))
else:
jobs.append(q.enqueue_call(func=processFile, args=(filepath, 2,), timeout=300))
print("Adding jobs to queue", bar, end='\r')
sys.stdout.flush()
print()
print("--- Waiting for return results")
bar = ProgressBar(len(filepaths), max_width=40)
results = []
bar.numerator = 1
while len(jobs) != 0:
for j in jobs:
filepaths = glob.glob('data/*/*.txt')
#random.shuffle(filepaths)
#filepaths = filepaths[0:1000]
num_tasks = len(filepaths)
bar = ProgressBar(num_tasks, max_width=40)
p = multiprocessing.Pool()
results = p.imap(create_data, filepaths)
print("--- Started processing")
while (True):
bar.numerator = results._index
print(bar, end='\r')
sys.stdout.flush()
time.sleep(1)
if (results._index == num_tasks): break
p.close()
p.join()
print()
df_full = pd.DataFrame(list(results))
print("--- Calculating link ratios")
#calculate counts for training data
sponsored_counts = {}
nonsponsored_counts = {}
for index, row in df_full.iterrows():
#https://celery.readthedocs.org/en/latest/reference/celery.result.html#celery.result.ResultSet
for k, filename in enumerate(list(train['file'])+list(test_files)):
if filename in train_keys:
result.add(processFile.delay(filename, train_keys[filename]))
elif filename != "":
result.add(processFile.delay(filename, 2))
#sponsored = train.loc[train['file'] == openfile]
#if not sponsored.empty:
#result.add(processFile.delay(openfile, data, int(sponsored['sponsored'])))
#testing = sample.loc[sample['file'] == openfile]
#if not testing.empty:
#result.add(processFile.delay(openfile, data, int(sponsored['sponsored'])))
bar.numerator = k
print("Sending out processes ", bar, end='\r')
sys.stdout.flush()
bar = ProgressBar(len(train)+len(test_files), max_width=40)
while not result.ready():
time.sleep(5)
bar.numerator = result.completed_count()
print("Waiting for return results ", bar, end='\r')
sys.stdout.flush()
results = result.join() #wait for jobs to finish
df_full = pd.DataFrame(list(results))
print('--- Training random forest')