def __init__(self, model="inception-v3", layer="penultimate",
server_url='api.garaza.io:443'):
super().__init__(server_url)
model_settings = self._get_model_settings_confidently(model, layer)
self._model = model
self._layer = layer
self._target_image_size = model_settings['target_image_size']
cache_file_path = self._cache_file_blueprint.format(model, layer)
self._cache_file_path = join(cache_dir(), cache_file_path)
self._cache_dict = self._init_cache()
self._session = cachecontrol.CacheControl(
requests.session(),
cache=cachecontrol.caches.FileCache(
join(cache_dir(), __name__ + ".ImageEmbedder.httpcache"))
)
# attribute that offers support for cancelling the embedding
# if ran in another thread
self.cancelled = False
self.machine_id = \
QSettings().value('error-reporting/machine-id', '', type=str) \
or str(uuid.getnode())
self.session_id = None
def __init__(self):
self._session = cachecontrol.CacheControl(
requests.session(),
cache=cachecontrol.caches.FileCache(
join(cache_dir(), __name__ + ".ImageEmbedder.httpcache")
),
)
def clear_caches(self): # pylint: disable=import-outside-toplevel
from Orange.misc import environ
log.info("Clearing caches")
self._rm_tree(environ.cache_dir())
log.info("Clearing data")
self._rm_tree(environ.data_dir(versioned=True))
self._rm_tree(environ.data_dir(versioned=False))
def _cache_init(self):
""" Initialize cache in Orange environment buffer dir. """
path = os.path.join(environ.cache_dir(), "nytcache")
try:
if not os.path.exists(path):
os.makedirs(path)
self.cache_path = os.path.join(path, "query_cache")
except OSError as e:
warnings.warn('Could not initialize NYT cache: {}'.format(str(e)), RuntimeWarning)
def __init__(self):
self.cache_dir = os.path.join(cache_dir(), 'ontology')
if not os.path.isdir(self.cache_dir):
os.makedirs(self.cache_dir)
self.similarities = dict()
try:
with open(os.path.join(self.cache_dir, 'sims.pkl'), 'rb') as file:
self.similarities = pickle.load(file)
except IOError:
self.similarities = dict()
self.embeddings = dict()
def __init__(self, model, layer, server_url='api.biolab.si:8080'):
super().__init__(server_url)
model_settings = self._get_model_settings_confidently(model, layer)
self._model = model
self._layer = layer
self._target_image_size = model_settings['target_image_size']
cache_file_path = self._cache_file_blueprint.format(model, layer)
self._cache_file_path = join(cache_dir(), cache_file_path)
self._cache_dict = self._init_cache()
self._session = cachecontrol.CacheControl(
requests.session(),
cache=cachecontrol.caches.FileCache(
join(cache_dir(), __name__ + ".ImageEmbedder.httpcache"))
)
# attribute that offers support for cancelling the embedding
# if ran in another thread
self.cancelled = False
def cache_dir():
"""
Return the Orange application cache directory. If the directory path
does not yet exists then create it.
"""
path = os.path.join(environ.cache_dir(), "canvas")
try:
os.makedirs(path, exist_ok=True)
except OSError:
pass
return path
def cache_dir():
"""Return the application cache directory. If the directory path
does not yet exists then create it.
"""
from Orange.misc import environ
path = os.path.join(environ.cache_dir(), "canvas")
if not os.path.isdir(path):
os.makedirs(path, exist_ok=True)
return path
def __init__(self, model, layer,
server_url='api.biolab.si', server_port=8080):
super().__init__(server_url, server_port)
model_settings = self._get_model_settings_confidently(model, layer)
self._model = model
self._layer = layer
self._target_image_size = model_settings['target_image_size']
cache_file_path = self._cache_file_blueprint.format(model, layer)
self._cache_file_path = join(cache_dir(), cache_file_path)
self._cache_dict = self._init_cache()
class CachedNodeInfoTable:
LIST = os.path.join(cache_dir(), 'owmonroedata', 'nodes_info_list.pickle')
TABLES = os.path.join(cache_dir(), 'owmonroedata', 'tables.pickle')
os.makedirs(os.path.dirname(LIST), exist_ok=True)
@classmethod
def _load(cls, file):
with open(file, 'rb') as f:
return pickle.load(f)
@classmethod
def _dump(cls, obj, file):
with open(file, 'wb') as f:
pickle.dump(obj, f, protocol=pickle.HIGHEST_PROTOCOL)
@classmethod
def load_list(cls):
return cls._load(cls.LIST)
@classmethod
def dump_list(cls, lst):
cls._dump(lst, cls.LIST)
@classmethod
def load_tables(cls):
return cls._load(cls.TABLES)
@classmethod
def dump_tables(cls, lst):
cls._dump(lst, cls.TABLES)
@classmethod
def clear(cls):
for file in (cls.LIST, cls.TABLES):
try:
os.remove(file)
except FileNotFoundError:
pass
def __init__(self, model, model_settings):
self.model = model
self._load_model()
self._target_image_size = model_settings["target_image_size"]
self._session = cachecontrol.CacheControl(
requests.session(),
cache=cachecontrol.caches.FileCache(
join(cache_dir(), __name__ + ".ImageEmbedder.httpcache")))
self.cancelled = False
self._image_loader = ImageLoader()
self._cache = EmbedderCache(model)
def __init__(self,
model,
layer,
server_url='api.biolab.si',
server_port=8080):
super().__init__(server_url, server_port)
model_settings = self._get_model_settings_confidently(model, layer)
self._model = model
self._layer = layer
self._target_image_size = model_settings['target_image_size']
cache_file_path = self._cache_file_blueprint.format(model, layer)
self._cache_file_path = join(cache_dir(), cache_file_path)
self._cache_dict = self._init_cache()
def __init__(self, model, model_settings, layer):
self.model = model
self.layer = layer
self._load_model()
self._target_image_size = model_settings["target_image_size"]
self._session = cachecontrol.CacheControl(
requests.session(),
cache=cachecontrol.caches.FileCache(
join(cache_dir(), __name__ + ".ImageEmbedder.httpcache"))
)
self.cancelled = False
self._image_loader = ImageLoader()
self._cache = EmbedderCache(model, layer)
def __init__(self, email, progress_callback=None, error_callback=None):
Entrez.email = email
self.record_id_list = None # Ids of the records available.
self.search_record_count = 0 # Number of records.
self.search_record_web_env = None # BioPython history param.
self.search_record_query_key = None # BioPython history param.
self.progress_callback = progress_callback
self.error_callback = error_callback
self.stop_signal = False
self.cache_path = None
cache_folder = os.path.join(environ.cache_dir(), 'pubmedcache')
if not os.path.exists(cache_folder):
os.makedirs(cache_folder)
self.cache_path = os.path.join(cache_folder, 'query_cache')
import warnings
from Orange.misc import environ
warnings.warn("'{}' is deprecated please do not import it".format(__name__),
DeprecationWarning,
stacklevel=2)
buffer_dir = environ.cache_dir()
widget_settings_dir = environ.widget_settings_dir()
from AnyQt.QtWidgets import QLineEdit
from AnyQt.QtWidgets import QSizePolicy as Policy
from AnyQt.QtCore import pyqtSignal
from Orange.misc import environ
from Orange.data import Table
from Orange.widgets.widget import OWWidget
from Orange.widgets import gui, settings
from Orange.widgets.utils.concurrent import ThreadExecutor, Task
from ..resolwe import ResolweAPI, to_orange_table, ResolweCredentialsException, ResolweServerException
error_red = 'QWidget { background-color:#FFCCCC;}'
# Support cache with requests_cache module
cache_path = os.path.join(environ.cache_dir(), "resolwe")
try:
os.makedirs(cache_path)
except OSError:
pass
cache_file = os.path.join(cache_path, 'vaccinesurvey_cache')
# cache successful requests for one hour
requests_cache.install_cache(cache_name=cache_file,
backend='sqlite',
expire_after=3600)
class OWImportSamples(OWWidget):
name = "Import Samples"
icon = "icons/import.svg"
want_main_area = False
def __init__(self,
iterations=None,
learning_rate=None,
depth=None,
l2_leaf_reg=None,
model_size_reg=None,
rsm=None,
loss_function=None,
border_count=None,
feature_border_type=None,
per_float_feature_quantization=None,
input_borders=None,
output_borders=None,
fold_permutation_block=None,
od_pval=None,
od_wait=None,
od_type=None,
nan_mode=None,
counter_calc_method=None,
leaf_estimation_iterations=None,
leaf_estimation_method=None,
thread_count=None,
random_seed=None,
use_best_model=None,
verbose=False,
logging_level=None,
metric_period=None,
ctr_leaf_count_limit=None,
store_all_simple_ctr=None,
max_ctr_complexity=None,
has_time=None,
allow_const_label=None,
classes_count=None,
class_weights=None,
one_hot_max_size=None,
random_strength=None,
name=None,
ignored_features=None,
train_dir=cache_dir(),
custom_loss=None,
custom_metric=None,
eval_metric=None,
bagging_temperature=None,
save_snapshot=None,
snapshot_file=None,
snapshot_interval=None,
fold_len_multiplier=None,
used_ram_limit=None,
gpu_ram_part=None,
allow_writing_files=False,
final_ctr_computation_mode=None,
approx_on_full_history=None,
boosting_type=None,
simple_ctr=None,
combinations_ctr=None,
per_feature_ctr=None,
task_type=None,
device_config=None,
devices=None,
bootstrap_type=None,
subsample=None,
sampling_unit=None,
dev_score_calc_obj_block_size=None,
max_depth=None,
n_estimators=None,
num_boost_round=None,
num_trees=None,
colsample_bylevel=None,
random_state=None,
reg_lambda=None,
objective=None,
eta=None,
max_bin=None,
scale_pos_weight=None,
gpu_cat_features_storage=None,
data_partition=None,
metadata=None,
early_stopping_rounds=None,
cat_features=None,
grow_policy=None,
min_data_in_leaf=None,
min_child_samples=None,
max_leaves=None,
num_leaves=None,
score_function=None,
leaf_estimation_backtracking=None,
ctr_history_unit=None,
monotone_constraints=None,
feature_weights=None,
penalties_coefficient=None,
first_feature_use_penalties=None,
model_shrink_rate=None,
model_shrink_mode=None,
langevin=None,
diffusion_temperature=None,
posterior_sampling=None,
boost_from_average=None,
text_features=None,
tokenizers=None,
dictionaries=None,
feature_calcers=None,
text_processing=None,
preprocessors=None):
super().__init__(preprocessors=preprocessors)
self.params = vars()
import warnings
from Orange.misc import environ
warnings.warn("'{}' is deprecated please do not import it"
.format(__name__),
DeprecationWarning, stacklevel=2)
buffer_dir = environ.cache_dir()
widget_settings_dir = environ.widget_settings_dir()
def __init__(self):
self._session = cachecontrol.CacheControl(
requests.session(),
cache=cachecontrol.caches.FileCache(
join(cache_dir(), __name__ + ".ImageEmbedder.httpcache"))
)
def __init__(self, model, layer):
# init the cache
cache_file_path = self._cache_file_blueprint.format(model, layer)
self._cache_file_path = join(cache_dir(), cache_file_path)
self._cache_dict = self._init_cache()
import Orange.distance
import Orange.misc
from Orange.misc.environ import cache_dir
from Orange.widgets import gui, settings
from Orange.widgets.settings import SettingProvider
from Orange.widgets.utils.sql import check_sql_input
from Orange.canvas import report
from Orange.widgets.visualize.owscatterplotgraph import OWScatterPlotGraph, InteractiveViewBox
from Orange.widgets.widget import Msg, OWWidget, Input, Output
from Orange.widgets.utils.annotated_data import (create_annotated_table,
create_groups_table,
ANNOTATED_DATA_SIGNAL_NAME)
RE_FIND_INDEX = r"(^{} \()(\d{{1,}})(\)$)"
tsne_cache = os.path.join(cache_dir(), "tsne")
memory = Memory(tsne_cache, verbose=0, bytes_limit=1e8)
memory.reduce_size()
###
### TODO: When the next two functions are released in Orange, import from there
def get_indices(names, name):
"""
Return list of indices which occur in a names list for a given name.
:param names: list of strings
:param name: str
:return: list of indices
"""
return [
def __init__(self, model, layer):
# init the cache
cache_file_path = self._cache_file_blueprint.format(model, layer)
self._cache_file_path = join(cache_dir(), cache_file_path)
self._cache_dict = self._init_cache()
