def test_split_out_both_servers_and_lb_nodes_if_too_long(self):
"""
Both 'lb_nodes' and 'servers' are split out if the event is too long
to accomodate both. The longest one is removed first.
"""
event = {
'hi': 'there',
'desired': self.state,
'steps': ['steps'],
'lb_nodes': ['1', '2', '3', '4'],
'servers': ['1', '2', '3']
}
message = "Executing convergence"
short_event = {
k: event[k]
for k in event if k not in ('servers', 'lb_nodes')
}
result = split_execute_convergence(
event.copy(),
max_length=len(json.dumps(short_event, default=repr)) + 5)
expected = [(short_event, message),
(dissoc(event, 'desired', 'steps', 'servers'), message),
(dissoc(event, 'desired', 'steps', 'lb_nodes'), message)]
self.assertEqual(result, expected)
def test_split_out_lb_nodes_if_lb_nodes_longer(self):
"""
If the 'lb_nodes' parameter is longer than the 'servers' parameter,
and the event is otherwise sufficiently small, 'lb_nodes' is the
param that gets split into another message.
"""
event = {
'hi': 'there',
'desired': self.state,
'steps': ['steps'],
'lb_nodes': ['1', '2', '3', '4'],
'servers': ['1', '2', '3']
}
message = "Executing convergence"
# assume that removing 'servers' would make it the perfect length, but
# since 'lb_nodes' is bigger, it's the thing that gets removed.
length = len(
json.dumps({k: event[k]
for k in event if k != 'servers'},
default=repr), )
result = split_execute_convergence(event.copy(), max_length=length)
expected = [(dissoc(event, 'lb_nodes'), message),
(dissoc(event, 'desired', 'steps', 'servers'), message)]
self.assertEqual(result, expected)
def test_dissoc():
assert dissoc({"a": 1}, "a") == {}
assert dissoc({"a": 1, "b": 2}, "a") == {"b": 2}
assert dissoc({"a": 1, "b": 2}, "b") == {"a": 1}
# Verify immutability:
d = {'x': 1}
oldd = d
d2 = dissoc(d, 'x')
assert d is oldd
assert d2 is not oldd
def test_dissoc():
assert dissoc({"a": 1}, "a") == {}
assert dissoc({"a": 1, "b": 2}, "a") == {"b": 2}
assert dissoc({"a": 1, "b": 2}, "b") == {"a": 1}
# Verify immutability:
d = {'x': 1}
oldd = d
d2 = dissoc(d, 'x')
assert d is oldd
assert d2 is not oldd
def test_dissoc(self):
D, kw = self.D, self.kw
assert dissoc(D({"a": 1}), "a") == D({})
assert dissoc(D({"a": 1, "b": 2}), "a") == D({"b": 2})
assert dissoc(D({"a": 1, "b": 2}), "b") == D({"a": 1})
# Verify immutability:
d = D({'x': 1})
oldd = d
d2 = dissoc(d, 'x')
assert d is oldd
assert d2 is not oldd
def test_dissoc(self):
D, kw = self.D, self.kw
assert dissoc(D({"a": 1}), "a") == D({})
assert dissoc(D({"a": 1, "b": 2}), "a") == D({"b": 2})
assert dissoc(D({"a": 1, "b": 2}), "b") == D({"a": 1})
# Verify immutability:
d = D({'x': 1})
oldd = d
d2 = dissoc(d, 'x')
assert d is oldd
assert d2 is not oldd
async def delete(self, id_):
self.items, has_changed = pipe(
id_,
lambda key: dissoc(self.items, key),
lambda new: (new, len(self.items) != len(new)),
)
return has_changed
def _dispatch(self, request: Tuple[str, ...]):
log.debug("got a request", request=request)
request_type, *request_arg = request
try:
if request_type == "submit":
job_name, fname = request_arg
log.debug("submitting a task", fname=fname, job_name=job_name)
job_id = self.submit(job_name, fname)
return job_id
elif request_type == "cancel":
job_id = request_arg[0]
log.debug("got a cancel request", job_id=job_id)
self.cancel(job_id)
return None
elif request_type == "queue":
log.debug("got a queue request")
# remove the "proc" entries because they aren't pickable
return {
job_id: dissoc(info, "proc")
for job_id, info in self.queue().items()
}
else:
log.debug("got unknown request")
except Exception as e:
return e
def main():
desc = _read_desc(_desc_path())
pprint(desc)
initial_state = {'desc': tzd.dissoc(desc, 'worker_pos'),
'worker': {'pos': desc['worker_pos'],
'orien': 'r'},
'wrapped_shells': [_pt2shell(desc['worker_pos'])]}
states = [initial_state]
actions = []
shutil.rmtree(_output_image_dir(_desc_path()), ignore_errors=True)
for turn_i in range(500):
print('- turn {}'.format(turn_i))
prev_state = states[turn_i]
action, intermediate_state = _predict_action(prev_state)
if turn_i % 50 == 0:
# if True:
_export_state(intermediate_state, turn_i, _desc_path(), draw_opts={'render_scale': 10})
if action is None:
break
next_state = _update_state(intermediate_state, action)
states.append(next_state)
actions.append(action)
_export_actions(actions, _output_actions_filepath(_desc_path()))
def main(db_uri, tablename):
click.echo("Connecting to database...")
database = SQLDatabase(db_uri, tablename)
click.echo("Checking existing number of evaluations...")
if len(database) < 2: # Check if there are enough existing datapoints
for point in initial_points:
target = black_box_function(**point)
database.append({**point, "target": target})
click.echo("Creating optimizer and utility function...")
optimizer = BayesianOptimization(f=None, pbounds=pbounds, random_state=1)
utility_function = UtilityFunction(kind="ucb", kappa=3, xi=1)
click.echo("Registering evaluations with optimizer...")
for evaluation in database:
optimizer.register(dissoc(evaluation, "target"), evaluation["target"])
while True:
try:
point = optimizer.suggest(utility_function)
click.echo(f"Evaluating black-box function for {point}")
target = black_box_function(**point)
click.echo(f"Got {target}")
click.echo("Registering with database...")
database.append({**point, "target": target})
click.echo("Registering with optimizer...")
optimizer.register(point, target)
except KeyboardInterrupt:
click.echo("Exiting...")
break
def pop_nan(self, dct):
"""Given dict, return dict with keys popped where isnan(val)."""
res = dict(dct)
nans = valfilter(
lambda x: (
x is None or str(x).strip() == '' or
(isinstance(x, (Decimal, float)) and isnan(x))), res)
return dissoc(res, *nans.keys())
def remove_factor(self, factor):
factor_name = factor if isinstance(factor, str) else factor.name
py_assert(factor_name in self.names, ValueError,
'unable to remove factor_name {0}, which does not exist in current container'.format(factor_name))
self.names.remove(factor_name)
self.data.drop(factor_name, axis=1, inplace=True)
self.property = dissoc(self.property, factor_name)
return
def parse(raw_form: str):
form_data = valmap(first, parse_qs(raw_form, strict_parsing=True))
return RsvpFormData(guest_id=form_data[RsvpFormData.GUEST_ID_FIELD],
party_id=form_data[RsvpFormData.PARTY_ID_FIELD],
attending=valmap(
_parse_bool,
dissoc(form_data, RsvpFormData.GUEST_ID_FIELD,
RsvpFormData.PARTY_ID_FIELD)))
def queue(self, only_me: bool = True):
# only_me doesn't do anything, but the argument is there
# because it is in the other schedulers.
# remove the "proc" entries because they aren't pickable
return {
job_id: dissoc(info, "proc")
for job_id, info in self._current_queue.items()
}
def remove_factor(self, factor):
factor_name = factor if isinstance(factor, str) else factor.name
py_assert(
factor_name in self.names, ValueError,
'unable to remove factor_name {0}, which does not exist in current container'
.format(factor_name))
self.names.remove(factor_name)
self.data.drop(factor_name, axis=1, inplace=True)
self.property = dissoc(self.property, factor_name)
return
def _get_user_site_details(user_data):
user = []
allowed_keys = {'user_id', 'site_url', 'site_name'}
remapped_keys = {'user_id': 'site_user_id'}
keys_to_dissoc = set(user_data['items'][0].keys()) - allowed_keys
for site in user_data['items']:
filtered_data = dissoc(site, *keys_to_dissoc)
remapped_data = apply_key_map(remapped_keys, filtered_data)
user.append(remapped_data)
return user
def fit(self, X, y=None, sample_weight=None, exposure=None):
fit_args = self._process_args(X=X,
y=y,
sample_weight=sample_weight,
exposure=exposure)
# Create internal cross-validating estimators
self.cross_validating_estimators_ = OrderedDict(
(k,
CrossValidatingEstimator(v,
cv=self.cv,
n_jobs=self.n_jobs,
verbose=self.verbose,
pre_dispatch=self.pre_dispatch))
for k, v in self.ordered_regressors.items())
# frozendict(valmap(lambda x:
# CrossValidatingEstimator(x, cv=self.cv, n_jobs=self.n_jobs,
# verbose=self.verbose,
# pre_dispatch=self.pre_dispatch), self.regressors).items())
# Fit the inner regressors using cross-validation
for est_name, est in self.cross_validating_estimators_.items():
if self.verbose > 0:
print('Super learner is fitting %s...' % est_name)
est.fit(**fit_args)
if self.verbose > 0:
print('Super learner finished fitting %s.' % est_name)
# Fit the outer meta-regressor. Cross validation is not used here. Instead,
# users of the SuperLearner are free to wrap the SuperLearner in a
# CrossValidatingEstimator.
meta_fit_args = assoc(
fit_args, 'X',
np.concatenate(tuple(
map(growd(2), [
est.cv_predictions_
for est in self.cross_validating_estimators_.values()
])),
axis=1))
if self.y_transformer is not None:
self.y_transformer_ = clone(self.y_transformer).fit(**fit_args)
meta_fit_args = assoc(
meta_fit_args, 'y',
self.y_transformer_.transform(
**dissoc(fit_args, 'sample_weight', 'y')))
if self.verbose > 0:
print('Super learner fitting meta-regressor...')
self.meta_regressor_ = clone(self.meta_regressor).fit(**meta_fit_args)
if self.verbose > 0:
print('Super learner meta-regressor fitting complete.')
# All scikit-learn compatible estimators must return self from fit
return self
def test_dicttoolz():
d1 = {'foo': 'bar'}
d2 = {'baz': 'quux'}
assert_that(merge(d1, d2)).is_equal_to({'foo': 'bar', 'baz': 'quux'})
assert_that(d1).is_equal_to({'foo': 'bar'})
assert_that(assoc(d1, 'a', 1)).is_equal_to({'foo': 'bar', 'a': 1})
assert_that(dissoc(d2, 'baz')).is_equal_to({})
struct = {'a': [{'c': 'hello'}]}
assert_that(get_in(['a', 0, 'c'], struct)).is_equal_to(struct['a'][0]['c'])
assert_that(get_in(['a', 0, 'd'], struct,
'not found')).is_equal_to('not found')
def as_effect(self):
"""Produce an :obj:`Effect` to update a stack."""
stack_config = dissoc(thaw(self.stack_config), 'stack_name')
eff = update_stack(stack_name=self.stack.name, stack_id=self.stack.id,
stack_args=stack_config)
def report_success(result):
retry_msg = 'Waiting for stack to update'
return ((StepResult.RETRY, [ErrorReason.String(retry_msg)])
if self.retry else (StepResult.SUCCESS, []))
return eff.on(success=report_success)
def as_effect(self):
"""Produce an :obj:`Effect` to update a stack."""
stack_config = dissoc(thaw(self.stack_config), 'stack_name')
eff = update_stack(stack_name=self.stack.name, stack_id=self.stack.id,
stack_args=stack_config)
def report_success(result):
retry_msg = 'Waiting for stack to update'
return ((StepResult.RETRY, [ErrorReason.String(retry_msg)])
if self.retry else (StepResult.SUCCESS, []))
return eff.on(success=report_success)
def test_split_out_both_servers_and_lb_nodes_if_too_long(self):
"""
Both 'lb_nodes' and 'servers' are split out if the event is too long
to accomodate both. The longest one is removed first.
"""
event = {'hi': 'there', 'desired': self.state, 'steps': ['steps'],
'lb_nodes': ['1', '2', '3', '4'], 'servers': ['1', '2', '3']}
message = "Executing convergence"
short_event = {k: event[k] for k in event
if k not in ('servers', 'lb_nodes')}
result = split_execute_convergence(
event.copy(),
max_length=len(json.dumps(short_event, default=repr)) + 5)
expected = [
(short_event, message),
(dissoc(event, 'desired', 'steps', 'servers'), message),
(dissoc(event, 'desired', 'steps', 'lb_nodes'), message)
]
self.assertEqual(result, expected)
def _predict_action(state):
mine = shapely.geometry.Polygon(state['desc']['mine_shell'])
obstacles = [shapely.geometry.Polygon(sh) for sh in state['desc']['obstacle_shells']]
obstacle = shapely.ops.unary_union(obstacles)
situable = mine.difference(obstacle)
wrappeds = [shapely.geometry.Polygon(sh) for sh in state['wrapped_shells']]
wrapped = shapely.ops.unary_union(wrappeds)
not_wrapped = situable.difference(wrapped)
if not_wrapped.area < 1.0:
return None, state
last_move = state.get('last_move', 'W')
for move in [last_move, 'W', 'S', 'A', 'D']:
proj = _move_projection_center(state['worker']['pos'], move)
if not_wrapped.contains(proj):
return move, tzd.dissoc(state, 'path_pts_to_not_wrapped')
if not state.get('path_pts_to_not_wrapped'):
target_tile = tzf.thread_first(not_wrapped.representative_point(),
_shapely_point2pt,
_snap_to_tile)
print('Finding shortest path from tile {} to {}'.format(state['worker']['pos'], target_tile))
if tzd.get_in(['cache', 'incidence_m'], state) is None:
incidence_m = _incidence_matrix(situable)
state = tzd.assoc_in(state, ['cache', 'incidence_m'], incidence_m)
else:
incidence_m = state['cache']['incidence_m']
target_vertex_ind = _incidence_ind(target_tile[0], target_tile[1], x_size=math.ceil(situable.bounds[2]))
path_dists, path_predecessors = sp.sparse.csgraph.shortest_path(csgraph=incidence_m,
directed=False,
return_predecessors=True,
unweighted=True,
indices=target_vertex_ind)
start_vertex_ind = _incidence_ind(state['worker']['pos'][0],
state['worker']['pos'][1],
x_size=math.ceil(situable.bounds[2]))
path_inds = _path_inds(path_predecessors, start_vertex_ind)
path_pts = [_incidence_pt(ind, x_size=math.ceil(situable.bounds[2]))
for ind in path_inds]
print('Found path: {}'.format(path_pts))
state = tzd.assoc(state, 'path_pts_to_not_wrapped', path_pts)
path_move = _projection_pt_move(state['worker']['pos'], state['path_pts_to_not_wrapped'][0])
if path_move is not None:
return path_move, tzd.update_in(state, ['path_pts_to_not_wrapped'], lambda p: p[1:])
return 'Z', state
def test_split_out_lb_nodes_if_lb_nodes_longer(self):
"""
If the 'lb_nodes' parameter is longer than the 'servers' parameter,
and the event is otherwise sufficiently small, 'lb_nodes' is the
param that gets split into another message.
"""
event = {
"hi": "there",
"desired": self.state,
"steps": ["steps"],
"lb_nodes": ["1", "2", "3", "4"],
"servers": ["1", "2", "3"],
}
message = "Executing convergence"
# assume that removing 'servers' would make it the perfect length, but
# since 'lb_nodes' is bigger, it's the thing that gets removed.
length = len(json.dumps({k: event[k] for k in event if k != "servers"}, default=repr))
result = split_execute_convergence(event.copy(), max_length=length)
expected = [(dissoc(event, "lb_nodes"), message), (dissoc(event, "desired", "steps", "servers"), message)]
self.assertEqual(result, expected)
def test_split_out_lb_nodes_if_lb_nodes_longer(self):
"""
If the 'lb_nodes' parameter is longer than the 'servers' parameter,
and the event is otherwise sufficiently small, 'lb_nodes' is the
param that gets split into another message.
"""
event = {'hi': 'there', 'desired': self.state, 'steps': ['steps'],
'lb_nodes': ['1', '2', '3', '4'], 'servers': ['1', '2', '3']}
message = "Executing convergence"
# assume that removing 'servers' would make it the perfect length, but
# since 'lb_nodes' is bigger, it's the thing that gets removed.
length = len(
json.dumps({k: event[k] for k in event if k != 'servers'},
default=repr),)
result = split_execute_convergence(event.copy(), max_length=length)
expected = [
(dissoc(event, 'lb_nodes'), message),
(dissoc(event, 'desired', 'steps', 'servers'), message)
]
self.assertEqual(result, expected)
def filterFeatures(match):
usableKeys = ['players', 'radiant_win', 'hero_id', 'player_slot']
isUsable = lambda k: k in usableKeys
toplvlFiltered = keyfilter(isUsable, match)
filteredPlayers = []
for player in toplvlFiltered['players']:
side = decideSide(player['player_slot'])
playerData = assoc(keyfilter(isUsable, player), 'team', side)
playerData = dissoc(playerData, 'player_slot')
filteredPlayers.append(playerData)
toplvlFiltered['players'] = filteredPlayers
return toplvlFiltered
def test_split_out_both_servers_and_lb_nodes_if_too_long(self):
"""
Both 'lb_nodes' and 'servers' are split out if the event is too long
to accomodate both. The longest one is removed first.
"""
event = {
"hi": "there",
"desired": self.state,
"steps": ["steps"],
"lb_nodes": ["1", "2", "3", "4"],
"servers": ["1", "2", "3"],
}
message = "Executing convergence"
short_event = {k: event[k] for k in event if k not in ("servers", "lb_nodes")}
result = split_execute_convergence(event.copy(), max_length=len(json.dumps(short_event, default=repr)) + 5)
expected = [
(short_event, message),
(dissoc(event, "desired", "steps", "servers"), message),
(dissoc(event, "desired", "steps", "lb_nodes"), message),
]
self.assertEqual(result, expected)
def test_insert(mock_client_class):
mock_mongo_client = mongomock.MongoClient()
mock_client_class.return_value = mock_mongo_client
INPUT_DOCS = [{'name': 'A'}, {'name': 'B'}]
(INPUT_DOCS
| beam.transforms.Map(lambda d: pymongo.InsertOne(d))
| beam.transforms.ParDo(
MongoBulkWriteFn(db_uri='mongodb://localhost/db',
collection_name='test_beam',
db_name='db',
order_writes=True)))
found_objs = mock_mongo_client.get_database('db')['test_beam'].find({})
no_id_objs = map(lambda o: dissoc(o, '_id'), found_objs)
assert no_id_objs == INPUT_DOCS
def wordcloud():
if DEVELOPMENT_MODE:
with open('wordcloud.json') as f:
return jsonify(json.load(f))
else:
graph = facebook.GraphAPI(access_token=FACEBOOK_USER_ACCESS_TOKEN,
version='2.7')
query_string = f'fields=feed.since({SINCE})' \
'{comments{comments{message,created_time,like_count},' \
'message,created_time,like_count,reactions},' \
'message,created_time,updated_time,reactions}'
endpoint_url = f'{FACEBOOK_GROUP_ID}?{query_string}'
feed = graph.request(endpoint_url).get('feed')
text = ''
for each in feed.get('data'):
message = each.get('message')
if message:
text += message
comments = each.get('comments')
if comments:
for comment in comments.get('data'):
text += comment.get('message')
comments_in_comment = comment.get('comments')
if comments_in_comment:
for comment_in_comment in comments_in_comment.get(
'data'):
text += comment_in_comment.get('message')
from pythainlp.rank import rank
from pythainlp.tokenize import word_tokenize
word_list = word_tokenize(text, engine='newmm')
word_count = rank(word_list)
from toolz.dicttoolz import dissoc
new_word_count = dissoc(word_count, ' ')
words = []
for each in new_word_count:
d = {'word': each, 'value': new_word_count[each]}
words.append(d)
return jsonify(words)
def test_delta_with_delay(self):
schedule = {
'start': {
'relative_timeshift': {
'delay': '1',
'time_units': TimeUnits.DAYS,
}
},
'periodical': {
'repeats': PeriodicalUnits.HOURLY,
'every': 1,
},
'timezone': 'Europe/Kiev',
}
delta, _ = schedule_delta(schedule)
abs_delta, _ = schedule_delta(dissoc(schedule, 'start'))
assert delta == 86400
assert abs_delta == 3600
def __exit__(self, exc_type, exc_value, exc_traceback):
if isinstance(exc_value, ModuleCacheValid) or \
exc_type is ModuleCacheValid or \
exc_value is ModuleCacheValid:
inspect.stack()[1][0].f_globals.update(self.moduledata)
return True
elif exc_value is None:
new_moduledata = valfilter(
complement(flip(isinstance)(ModuleType)),
dissoc(inspect.stack()[1][0].f_globals, *self.suppress))
# Check that all objects can be cached
for _ in starmap(self._check_cachability, new_moduledata.items()):
pass
new_metadata = self.invalidator.new_metadata(new_moduledata)
self._put_in_cache(new_metadata, new_moduledata)
return True
else:
return False
def __sub__(self, key: A):
return Map(dicttoolz.dissoc(self, key))
def test_outputs():
for _name, task in encoded_dag['tasks'].items():
output = task['output']
assert output == sha(bencode(dissoc(task, 'output')))
def test_is_required(self, valid_create_hero_dto):
with pytest.raises(ValidationError) as excinfo:
CreateHeroDto(**dissoc(valid_create_hero_dto, "location"))
self.assert_validation_error("value_error.missing", excinfo)
def as_json(self):
"""
:return: a JSON dictionary representing the node.
"""
return dissoc(attr.asdict(self), "feed_events")
def test_is_optional(self, valid_update_hero_dto):
assert UpdateHeroDto(**dissoc(valid_update_hero_dto, "location"))
def test_is_required(self, valid_hero):
with pytest.raises(ValidationError) as excinfo:
Hero(**dissoc(valid_hero, "power_class"))
self.assert_validation_error("value_error.missing", excinfo)
def test_defaults(self, valid_update_hero_dto):
assert (UpdateHeroDto(
**dissoc(valid_update_hero_dto, "name")).name == "Unknown")
def test_defaults(self, valid_hero):
assert Hero(**dissoc(valid_hero, "name")).name == "Unknown"
def dump(self):
return dissoc(self.__dict__, '_sa_instance_state')
def test_is_optional(self, valid_update_hero_dto):
assert UpdateHeroDto(
**dissoc(valid_update_hero_dto, "power_class"))