class Topology:
"""
Provides methods to work on topology related features
Attribues:
G: graph of the network
T: minimum spanning tree of the network
paths: collection of paths
hosts: information about hosts
flows: information about flows
"""
def __init__(self):
"""
Initialize the topology with an empty graph and set of paths
"""
self.G = nx.Graph()
self.paths = Paths(self.G)
self.reset()
self.hosts = dict()
self.flows = dict()
def reset(self):
"""
Recompute minimum spanning tree and reset paths
"""
self.T = nx.minimum_spanning_tree(self.G)
self.paths.reset()
def __init__(self, path=None, create=False):
def pool_realpath(p):
return join(realpath(p), ".pool")
def get_default_path(create):
path_cwd = os.getcwd()
path_env = os.environ.get("POOL_DIR") or path_cwd
if create:
if isdir(pool_realpath(path_env)):
return path_cwd
else:
return path_env
else:
if isdir(pool_realpath(path_cwd)):
return path_cwd
else:
return path_env
if path is None:
path = get_default_path(create)
path = pool_realpath(path)
Paths.__init__(self, path)
def __init__(self, path=None, create=False):
def pool_realpath(p):
return join(realpath(p), ".pool")
def get_default_path(create):
path_cwd = os.getcwd()
path_env = os.environ.get("POOL_DIR") or path_cwd
if create:
if isdir(pool_realpath(path_env)):
return path_cwd
else:
return path_env
else:
if isdir(pool_realpath(path_cwd)):
return path_cwd
else:
return path_env
if path is None:
path = get_default_path(create)
path = pool_realpath(path)
Paths.__init__(self, path)
def load_database_file(self): Paths.create_paths() try: self.DEVICES = pickle.load(open(Paths.DATABASE_FILE, "rb")) except FileNotFoundError: # Create an empty database file if non exists pickle.dump([], open(Paths.DATABASE_FILE, "wb")) self.load_database_file()
def __init__(self, material, thickness, flipped=False):
super().__init__()
self._cuts = Paths()
self._lines = Paths()
self._regions = []
self._material = material
self._thickness = thickness
self._flipped = flipped
def __init__(self):
"""
Initialize the topology with an empty graph and set of paths
"""
self.G = nx.Graph()
self.paths = Paths(self.G)
self.reset()
self.hosts = dict()
self.flows = dict()
def load_log_filename(self):
Paths.create_paths()
try:
with open(Paths.LOG_PATHNAME_FILE, "rb") as f:
log_file = f.read().decode("UTF-8")
f.close()
return log_file
except FileNotFoundError:
return None
def export2txt_words4exam(words4exam: List[str]):
"""TODO 連日に渡るIT用語の重複回避を分析する用途として用語リストを保存する."""
dirpath = Paths().DIR_words4analyze
# ファイル出力用のディレクトリが存在しない場合、新規作成する
make_newdir(dirpath)
# 用語リストを本日付けで保存する
newfile = Paths().gen_FILE_WORD_LIST
shutil.copy(Paths().PATH_template_1st, dirpath + newfile)
def read_password_hash(self):
Paths.create_paths()
try:
with open(Paths.PASSWORD_FILE, "rb") as f:
password_hash = f.read()
f.close()
return password_hash
except FileNotFoundError:
if self.LOGGER is not None:
self.LOGGER.log("Password file doesn't exist")
return None
def upload2slack(dirpath: str, newfile: str):
"""生成したファイルを指定のSlackチャンネルに送信する"""
# 入力チェック
if dirpath is None:
# dirpath をデフォルトで設定する
dirpath = Paths().DIR_exam_papers
if newfile is None:
newfile = Paths().FILE_EXAM_PAPER
# 本日付けの確認テスト用ファイルを探索し、存在しない場合は異常終了
if not os.path.exists(dirpath + newfile):
print(' Today\'s file not found: ')
sys.exit(1)
URL_UPLOAD = "https://slack.com/api/files.upload"
with open(dirpath + newfile, 'rb') as f:
f = {'file': f.read()}
p = {
'token': env.TOKEN,
'channels': env.CHANNEL_ID,
'filename': newfile,
'filetype': 'md',
'initial_comment': "―【説明】―――――――――――――――――\
\n *添付ファイルをダウンロード後、以下を行いアップロードして下さい。*\
\n 1. 解答欄に記述\
\n 2. 記述後、ファイルを保存\
\n 3. ファイル名の変更（\"~_name.md\" ← name を変更する）\
\n\
\n―【解答方法】―――――――――――――――\
\n *合計 10P に達するように、解答用紙記載の用語リストを用いて文章を作成せよ。*\
\n ・参考リンクに記載のサイト等を利用し、自身で意味を調べて解答すること。\
\n ・１つの文章は、句点までを１文とみなす。\
\n ・１文あたり何語使用するかで加点が異なる。\
\n - １語のみ ： 1P UP↑\
\n - ２語 ： 3P UP↑\
\n - ３語以上 ： 5P UP↑\
\n ・用語リスト内から選択する際、１語以上であれば使用語数に制限はない。\
\n ・各文章間で用語が重複している場合、その文章は無効とする。\
\n ",
'title': "解答用紙_IT用語テスト",
}
r = requests.post(url=URL_UPLOAD, params=p, files=f)
if str(r.status_code) == '200':
print(' Uploaded.')
else:
print(' Upload_failed: ', r)
return
def __init__(self, name, dir_path=None):
self._logger = Logger("Fixture {0}".format(name))
path_name = Paths().get_fixture_path(name, only_name=True)
if dir_path:
conf_rel_path = Paths().fixture_conf_file_rel_pattern.format(path_name)
self._conf_path = os.path.join(dir_path, conf_rel_path)
else:
self._conf_path = Paths().get_fixture_path(name)
self.model = self._load_model(name)
self.history = grid_history.GriddingHistory(self)
def __init__(self):
self.paths = Paths()
self.data_conf = ConfigParser.SafeConfigParser()
self.read()
def generate_and_save_image(plots_data,
iter_no,
image_label,
scatter_size=0.5,
log=False):
if log:
print('------------------------------------------------------------')
print('%s: step %i: started generation' % (exp_name, iter_no))
figure = viz_utils.get_figure(plots_data, scatter_size)
if log:
print('%s: step %i: got figure' % (exp_name, iter_no))
figure_name = '%s-%05d.png' % (image_label, iter_no)
figure_path = Paths.get_result_path(figure_name)
figure.savefig(figure_path)
plt.close(figure)
img = np.array(im.imread(figure_path), dtype=np.uint8)
img = img[:, :, :-1]
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, 0)
if log:
print('%s: step %i: visualization saved' % (exp_name, iter_no))
print('------------------------------------------------------------')
return img, iter_no
def visualize_embeddings(node, split, threshold, iter_no, phase=None):
with tr.no_grad():
if split == 'train':
data = dl_set[node.id].data[split]
elif split == 'test':
data = x_seed
Z = node.post_gmm_encode(data)
labels = node.gmm_predict_test(Z, threshold).tolist()
pca_z = PCA(n_components=2)
z_transformed = pca_z.fit_transform(Z)
color = ['r', 'b', 'g']
colors = [color[int(x)] for x in labels]
b = 20
fig = plt.figure(figsize=(6.5, 6.5))
ax = fig.add_subplot(111)
ax.set_xlim(-b, b)
ax.set_ylim(-b, b)
ax.scatter(z_transformed[:, 0], z_transformed[:, 1], s=0.5, c=colors)
node.trainer.writer[split].add_figure(
node.name + '_' + phase + '_plots', fig, iter_no)
path = Paths.get_result_path(node.name + '_' + split +
'_embedding_plots/' + phase +
'_plot_%03d' % (iter_no))
fig.savefig(path)
plt.close(fig)
def update_password_hash(self, cleartext_password):
# Generate the hash
salt_handler = SaltHandler()
password_handler = PasswordHandler(salt_handler, cleartext_password)
key = password_handler.generate()
# Write the hash to the file
Paths.create_paths()
with open(Paths.PASSWORD_FILE, "wb") as f:
f.write(key)
f.close()
# Log feedback
if self.LOGGER is not None:
self.LOGGER.log("Password was updated")
def __init__(self, quiet): from os.path import isfile # Get a name for the log file log_file = self.load_log_filename() if log_file is None: # If no name is stored on the disk, generate a new one. log_file = Paths.LOG_DIR + self.generate_log_name() # Ensure that log file exists if not isfile(log_file): Paths.create_paths() open(log_file, "wb").close() # Initialize empty file self.LOG_FILE = log_file self.dump_log_filename() self.QUIET = quiet
def plot_mean_axis_distribution(node, split, iter_no, phase):
mean0 = node.kmeans.means[0]
mean1 = node.kmeans.means[1]
direction = (mean1 - mean0) / np.linalg.norm(mean1 - mean0)
if split == 'train':
data = dl_set[node.id].data['train']
elif split == 'test':
data = x_seed
Z = node.post_gmm_encode(data)
projection = np.zeros(Z.shape)
for j in range(Z.shape[0]):
projection[j] = mean0 + direction * np.dot(Z[j] - mean0, direction)
for i in range(projection.shape[1]):
plot_data_tensorboard = projection[:, i]
plot_data = [projection[:, i], mean0[i], mean1[i]]
plt.hist(plot_data, color = ['g', 'r', 'b'])
# plt.hist(plot_data_tensorboard, bins = 'auto', color = ['g'])
fig_mean_axis_histogram = plt.gcf()
node.trainer.writer[split].add_histogram(node.name + '_' + phase + '_mean_axis_' + str(i), plot_data_tensorboard, iter_no)
# node.trainer.writer[split].add_image(node.name + '_mean_axis_' + str(i), fig_mean_axis_histogram, iter_no)
path_mean_axis_hist = Paths.get_result_path(node.name + '_' + split + '_mean_axis_histogram/' + phase + '%03d_%01d' % (iter_no, i))
fig_mean_axis_histogram.savefig(path_mean_axis_hist)
plt.close(fig_mean_axis_histogram)
def load_node(node_name, tag=None, iter=None):
filename = node_name
if tag is not None:
filename += '_' + str(tag)
if iter is not None:
filename += ('_%05d' % iter)
filepath = os.path.join(Paths.weight_dir_path(''), filename)
gnode = GNode.load(filepath, Model=ImgGAN)
return gnode
def listen(self):
# Initialize pyudev monitor
ctx = Context()
mon = Monitor.from_netlink(ctx)
mon.start()
# Start listening and send all new connections to another thread
LOGGER.log("Listening for USB devices")
try:
for dev in iter(mon.poll, None):
connection_thread = Thread(target=Listener.connection,
args=[self, dev])
connection_thread.daemon = True
connection_thread.start()
except KeyboardInterrupt:
LOGGER.log("Exited by user!")
Paths.delete_tmp_dir()
exit()
def save_node(node, tag=None, iter=None):
# type: (GNode, str, int) -> None
filename = node.name
if tag is not None:
filename += '_' + str(tag)
if iter is not None:
filename += ('_%05d' % iter)
filename = filename + '.pt'
filepath = os.path.join(Paths.weight_dir_path(''), filename)
node.save(filepath)
def export2md_paper(words4exam: List[str]):
dirpath = Paths().DIR_exam_papers
# ファイル出力用のディレクトリが存在しない場合、新規作成する
make_newdir(dirpath)
# テンプレをコピー＆本日付けで別ファイルとして保存する
newfile = Paths().gen_FILE_EXAM_PAPER()
shutil.copy(Paths().PATH_template_1st, dirpath + newfile)
# 用語リストの箇所に引数で受け取った用語を書き込む
with open(dirpath + newfile, 'a', encoding='utf-8') as fst:
for word in words4exam:
fst.write('- ' + word + '\n')
with open(Paths().PATH_template_2nd, 'r', encoding='utf-8') as snd:
read_data = snd.read()
fst.write(read_data)
print(' Exported.')
upload2slack(dirpath, newfile)
return
def add_path(self, aper, *path):
if self._expansion > 0.0:
if isinstance(aper, tuple):
s = gerbertools.Shape(1e6)
s.append_int(aper)
s = s.offset(self._expansion, True)
assert len(s) == 1
aper = tuple(s.get_int(0))
else:
aper += from_mm(self._expansion)
paths = self._paths.get(aper, None)
# Due to roundoff error during rotation, some almost-identical
# (actually identical in the gerber file) apertures can appear
# for region apertures. To avoid this, look for apertures that
# are "close enough".
if paths is None and isinstance(aper, tuple):
for ap2 in self._paths:
if not isinstance(ap2, tuple):
continue
if len(aper) != len(ap2):
continue
err = 0
for c1, c2 in zip(aper, ap2):
err += (c1[0] - c2[0])**2
err += (c1[1] - c2[1])**2
if err > 100:
break
else:
aper = ap2
paths = self._paths[aper]
break
if paths is None:
paths = Paths()
self._paths[aper] = paths
paths.add(*path)
def fill_model(self, model):
fixture_name = model['fixture']
if fixture_name in self:
fixture = self[fixture_name]
model['im-original-scale'] = fixture.model.scale
model['fixture-file'] = fixture.path
else:
model['im-original-scale'] = 1.0
model['im-scale'] = 1.0
model['fixture-file'] = Paths().get_fixture_path(model['fixture'], only_name=True)
def update(self):
directory = Paths().fixtures
extension = ".config"
list_fixtures = map(lambda x: x.split(extension, 1)[0],
[fixture for fixture in os.listdir(directory)
if fixture.lower().endswith(extension)])
self._fixtures = dict()
for f in list_fixtures:
if f.lower() != "fixture":
fixture = FixtureSettings(f, directory)
self._fixtures[fixture.model.name] = fixture
def v_slide(params):
"""
"""
paths = Paths()
try:
try:
scn_file = OpenSlide(paths.slice_80)
except OpenSlideUnsupportedFormatError:
logging.error("OpenSlideUnsupportedFormatError!")
return
except OpenSlideError:
logging.error("OpenSlideError!")
return
start_point = params["start_point"]
x0 = start_point[0]
y0 = start_point[1]
bound_y = params["bound_y"]
tile_path = params["tile_path"]
save_tiles = params["save_tiles"]
q = params["queue"]
AVG_THRESHOLD = 170
pid = os.getpid()
data = {}
while y0 < bound_y:
img = scn_file.read_region((x0, y0), 0, (299, 299))
green_c_avg = np.average(np.array(img)[:, :, 1])
if green_c_avg < AVG_THRESHOLD:
sufix = "_" + str(x0) + "_" + \
str(y0) + ".png"
file_name = "scn80" + sufix
img = np.array(img)
img = img[:, :, 0:3]
data['pred'] = img
data['xlabel'] = np.array([x0])
data['ylabel'] = np.array([y0])
q.put(dict(data))
if save_tiles:
img.save(os.path.join(tile_path, file_name))
y0 += 150
return pid
finally:
scn_file.close()
def __init__(self, conf, actions_options, time_units, edit):
wx.Dialog.__init__(self, None, title=_('Add action'), size=(330, 290))
panel = wx.Panel(self)
self.conf = conf
self.actions_options = actions_options
list_actions = []
for i in self.actions_options:
list_actions.append(i[0])
wx.StaticText(panel, label=_('action'), pos=(10, 10))
self.action_select = wx.ComboBox(panel,
choices=list_actions,
style=wx.CB_READONLY,
size=(310, 32),
pos=(10, 35))
self.action_select.Bind(wx.EVT_COMBOBOX, self.onSelect)
wx.StaticText(panel, label=_('data'), pos=(10, 70))
self.data = wx.TextCtrl(panel, size=(310, 32), pos=(10, 95))
wx.StaticText(panel, label=_('repeat after'), pos=(10, 130))
self.repeat = wx.TextCtrl(panel, size=(150, 32), pos=(10, 155))
self.repeat.Disable()
self.repeat_unit = wx.ComboBox(panel,
choices=time_units,
style=wx.CB_READONLY,
size=(150, 32),
pos=(170, 155))
self.repeat_unit.Bind(wx.EVT_COMBOBOX, self.onSelectUnit)
self.repeat_unit.SetValue(_('no repeat'))
if edit != 0:
self.action_select.SetValue(list_actions[edit[1]])
self.data.SetValue(edit[2])
if edit[3] != 0.0:
self.repeat.SetValue(str(edit[3]))
self.repeat.Enable()
self.repeat_unit.SetValue(time_units[edit[4]])
cancelBtn = wx.Button(panel, wx.ID_CANCEL, pos=(70, 205))
okBtn = wx.Button(panel, wx.ID_OK, pos=(180, 205))
paths = Paths()
self.home = paths.home
self.currentpath = paths.currentpath
def z_histogram_plot(node, split, iter_no, phase):
with tr.no_grad():
if split == 'train':
data = dl_set[node.id].data[split]
elif split == 'test':
data = x_seed
Z = node.post_gmm_encode(data)
for i in range(Z.shape[1]):
plot_data = Z[:, i]
plt.hist(plot_data)
fig_histogram = plt.gcf()
node.trainer.writer[split].add_histogram(node.name + '_' + phase + '_embedding_' + str(i), plot_data, iter_no)
path_embedding_hist = Paths.get_result_path(node.name + '_' + split + '_embedding_histogram/' + phase + 'embedding_%03d_%01d' % (iter_no, i))
fig_histogram.savefig(path_embedding_hist)
plt.close(fig_histogram)
def __init__(self, language):
paths=Paths()
gettext.install('openplotter', paths.currentpath+'/locale', unicode=False)
presLan_en = gettext.translation('openplotter', paths.currentpath+'/locale', languages=['en'])
presLan_ca = gettext.translation('openplotter', paths.currentpath+'/locale', languages=['ca'])
presLan_es = gettext.translation('openplotter', paths.currentpath+'/locale', languages=['es'])
presLan_fr = gettext.translation('openplotter', paths.currentpath+'/locale', languages=['fr'])
presLan_nl = gettext.translation('openplotter', paths.currentpath+'/locale', languages=['nl'])
presLan_de = gettext.translation('openplotter', paths.currentpath+'/locale', languages=['de'])
if language=='en':presLan_en.install()
if language=='ca':presLan_ca.install()
if language=='es':presLan_es.install()
if language=='fr':presLan_fr.install()
if language=='nl':presLan_nl.install()
if language=='de':presLan_de.install()
def main():
args = parse_args()
paths = Paths()
checkpoints_path = str(paths.CHECKPOINTS_PATH)
logging_path = str(paths.LOG_PATH)
callbacks = [PrintCallback()]
checkpoint_callback = ModelCheckpoint(filepath=checkpoints_path +
'/{epoch}-{val_acc:.3f}',
save_top_k=True,
verbose=True,
monitor='val_acc',
mode='max',
prefix='')
early_stop_callback = EarlyStopping(monitor='val_acc',
mode='max',
verbose=False,
strict=False,
min_delta=0.0,
patience=2)
gpus = gpu_count()
log_save_interval = args.log_save_interval
logger = TensorBoardLogger(save_dir=logging_path, name='tuna-log')
logger.log_hyperparams(args)
max_epochs = args.epochs
model = LeNet(hparams=args, paths=paths)
trainer = Trainer(
callbacks=callbacks,
checkpoint_callback=checkpoint_callback,
early_stop_callback=early_stop_callback,
fast_dev_run=True,
gpus=gpus,
log_save_interval=log_save_interval,
logger=logger,
max_epochs=max_epochs,
min_epochs=1,
show_progress_bar=True,
weights_summary='full',
)
trainer.fit(model)
def get_marker_path(self):
paths = Paths()
if self.model.orentation_mark_path:
marker_paths = (self.model.orentation_mark_path,
os.path.join(paths.images, os.path.basename(self.model.orentation_mark_path)),
paths.marker)
else:
marker_paths = (paths.marker,)
for path in marker_paths:
try:
with open(path, 'rb') as _:
self._logger.info("Using marker at '{0}'".format(path))
return path
except IOError:
self._logger.warning("The designated orientation marker file does not exist ({0})".format(path))
return None
def _configure(self):
#################################
# Global logging level
#################################
p = self.p
u.verbose.set_level(p.verbose_level)
#################################
# Global data type switch
#################################
self.data_type = p.data_type
assert p.data_type in ['single', 'double']
self.FType = np.dtype(
'f' + str(np.dtype(np.typeDict[p.data_type]).itemsize)).type
self.CType = np.dtype(
'c' + str(2 * np.dtype(np.typeDict[p.data_type]).itemsize)).type
logger.info(_('Data type', self.data_type))
#################################
# Prepare interaction server
#################################
if parallel.master:
# Create the inteaction server
self.interactor = interaction.Server(p.interaction)
# Start the thread
self.interactor.activate()
# Register self as an accessible object for the client
self.interactor.objects['Ptycho'] = self
# Check if there is already a runtime container
if not hasattr(self, 'runtime'):
self.runtime = u.Param()
# Generate all the paths
self.paths = Paths(self.p.paths, self.runtime)
def __init__(self,root="."):
self.root = os.path.abspath(root)
self.paths = Paths(root=self.root)
self.extensions = Extensions()
self.aliases = {}
class Crawl:
def __init__(self,root="."):
self.root = os.path.abspath(root)
self.paths = Paths(root=self.root)
self.extensions = Extensions()
self.aliases = {}
def prepend_paths(self,*paths):
new = Paths(paths)
new.extend(self.paths)
self.paths = new
def prepend_path(self,*paths):
self.prepend_paths(*paths)
def append_paths(self,*paths):
for path in paths:
self.paths.append(path)
def append_path(self,*paths):
self.append_paths(*paths)
def remove_path(self,path):
if path in self.paths:
self.paths.remove(path)
def prepend_extensions(self,*extensions):
new = Extensions(extensions)
new.extend(self.extensions)
self.extensions = new
def prepend_extension(self,*extensions):
self.prepend_extensions(*extensions)
def append_extensions(self,*extensions):
for extension in extensions:
self.extensions.append(extension)
def append_extension(self,*extensions):
self.append_extensions(*extensions)
def remove_extension(self,extension):
if extension in self.extensions:
self.extensions.remove(extension)
def alias_extension(self,new_extension,old_extension):
new_extension = self.extensions.normalize_element(new_extension)
self.aliases[new_extension] = self.extensions.normalize_element(old_extension)
def unalias_extension(self,extension):
del self.aliases[self.extensions.normalize_element(extension)]
def find(self,*args,**kwargs):
return self.index().find(*args,**kwargs)
def index(self):
return Index(self.root,self.paths,self.extensions,self.aliases)
def entries(self,*args):
return self.index().entries(*args)
def stat(self,*args):
return self.index().stat(*args)
def dump_log_filename(self):
Paths.create_paths()
with open(Paths.LOG_PATHNAME_FILE, "wb") as f:
f.write(self.LOG_FILE.encode("UTF-8"))
f.close()
def __init__(self, path=None):
path = join(dirname(realpath(path)), ".deck")
Paths.__init__(self, path)
def prepend_paths(self,*paths): new = Paths(paths) new.extend(self.paths) self.paths = new
def __init__(self, path=None):
if path is None:
path = self.PATH
Paths.__init__(self, path)
def plot_cluster_graphs(node, split, threshold, iter_no, phase):
no_of_classes = H.no_of_classes
with tr.no_grad():
if split == 'train':
data = dl_set[node.id].data[split]
labels = dl_set[node.id].labels[split]
elif split == 'test':
data = x_seed
labels = l_seed
Z = node.post_gmm_encode(data)
if split == 'train':
p = node.kmeans.pred
elif split == 'test':
p = node.gmm_predict_test(Z, threshold)
""" plot the count of unassigned vs assigned labels
purple -- unassigned
green -- assigned """
unassigned_labels = [0 for i in range(no_of_classes)]
assigned_labels = [0 for i in range(no_of_classes)]
for i in range(len(p)):
if p[i] == 2:
unassigned_labels[labels[i]] += 1
else:
assigned_labels[labels[i]] += 1
barWidth = 0.3
r1 = np.arange(len(unassigned_labels))
r2 = [x + barWidth for x in r1]
plt.bar(r1,
unassigned_labels,
width=barWidth,
color='purple',
edgecolor='black',
capsize=7)
plt.bar(r2,
assigned_labels,
width=barWidth,
color='green',
edgecolor='black',
capsize=7)
plt.xticks([r + barWidth for r in range(len(unassigned_labels))],
[str(i) for i in range(no_of_classes)])
plt.ylabel('count')
fig_assigned = plt.gcf()
node.trainer.writer[split].add_figure(
node.name + '_' + phase + '_assigned_labels_count', fig_assigned,
iter_no)
path_assign = Paths.get_result_path(node.name + '_' + split +
'_assigned/' + phase +
'assigned_%03d' % (iter_no))
fig_assigned.savefig(path_assign)
plt.close(fig_assigned)
""" plot the percentage of assigned labels in cluster 0 and cluster 1
red -- cluster 0
blue -- cluster 1 """
l_seed_ch0 = labels[np.where(p == 0)]
l_seed_ch1 = labels[np.where(p == 1)]
count_ch0 = [0 for i in range(no_of_classes)]
count_ch1 = [0 for i in range(no_of_classes)]
prob_ch0 = [0 for i in range(no_of_classes)]
prob_ch1 = [0 for i in range(no_of_classes)]
for i in l_seed_ch0:
count_ch0[i] += 1
for i in l_seed_ch1:
count_ch1[i] += 1
for i in range(no_of_classes):
if (count_ch0[i] + count_ch1[i]) != 0:
prob_ch0[i] = count_ch0[i] * 1.0 / (count_ch0[i] +
count_ch1[i])
prob_ch1[i] = count_ch1[i] * 1.0 / (count_ch0[i] +
count_ch1[i])
else:
prob_ch0[i] = 0
prob_ch1[i] = 0
plt.bar(r1,
prob_ch0,
width=barWidth,
color='red',
edgecolor='black',
capsize=7)
plt.bar(r2,
prob_ch1,
width=barWidth,
color='blue',
edgecolor='black',
capsize=7)
plt.xticks([r + barWidth for r in range(len(prob_ch0))],
[str(i) for i in range(no_of_classes)])
plt.ylabel('percentage')
fig_confidence = plt.gcf()
node.trainer.writer[split].add_figure(
node.name + '_' + phase + '_confidence', fig_confidence, iter_no)
path_confidence = Paths.get_result_path(node.name + '_' + split +
'_confidence/' + phase +
'confidence_%03d' % (iter_no))
fig_confidence.savefig(path_confidence)
plt.close(fig_confidence)
""" get count of points that exceed the threshold of phase 1 part 2 """
aboveThresholdLabels_ch0 = [0 for i in range(no_of_classes)]
aboveThresholdLabels_ch1 = [0 for i in range(no_of_classes)]
for i in range(len(p)):
if p[i] == 0:
if (distance.mahalanobis(Z[i], node.kmeans.means[0],
node.kmeans.covs[0])) > threshold:
aboveThresholdLabels_ch0[labels[i]] += 1
elif p[i] == 1:
if (distance.mahalanobis(Z[i], node.kmeans.means[1],
node.kmeans.covs[1])) > threshold:
aboveThresholdLabels_ch1[labels[i]] += 1
plt.bar(r1,
aboveThresholdLabels_ch0,
width=barWidth,
color='red',
edgecolor='black',
capsize=7)
plt.bar(r2,
aboveThresholdLabels_ch1,
width=barWidth,
color='blue',
edgecolor='black',
capsize=7)
plt.xticks(
[r + barWidth for r in range(len(aboveThresholdLabels_ch0))],
[str(i) for i in range(no_of_classes)])
plt.ylabel('count')
fig_above_threshold = plt.gcf()
node.trainer.writer[split].add_figure(
node.name + '_' + phase + '_above_threshold', fig_above_threshold,
iter_no)
path_above_threshold = Paths.get_result_path(node.name + '_' + split +
'_above_threshold/' +
phase + '%03d' %
(iter_no))
fig_above_threshold.savefig(path_above_threshold)
plt.close(fig_above_threshold)
def __init__(self, backup_root=None):
if backup_root is None:
backup_root = '/'
Paths.__init__(self, join(backup_root, self.PATH))
