def run_mds(matrix: DistMatrix, max_iter: int, step_size: int, init_type: int,
embedding: np.ndarray, state: TaskState):
res = Result(embedding=embedding)
iterations_done = 0
init = embedding
state.set_status("Running...")
oldstress = np.finfo(np.float).max
while True:
step_iter = min(max_iter - iterations_done, step_size)
mds = MDS(
dissimilarity="precomputed", n_components=2,
n_init=1, max_iter=step_iter,
init_type=init_type, init_data=init
)
mdsfit = mds(matrix)
iterations_done += step_iter
embedding, stress = mdsfit.embedding_, mdsfit.stress_
emb_norm = np.sqrt(np.sum(embedding ** 2, axis=1)).sum()
if emb_norm > 0:
stress /= emb_norm
res.embedding = embedding
state.set_partial_result(res)
state.set_progress_value(100 * iterations_done / max_iter)
if iterations_done >= max_iter or stress == 0 or \
(oldstress - stress) < mds.params["eps"]:
return res
init = embedding
oldstress = stress
if state.is_interruption_requested():
return res
def _prepare_dir_and_save_images(paths_queue, dir_name, target_size,
previously_saved, state: TaskState):
"""
This function prepares a directory structure and calls function
that saves images.
Parameters
----------
previously_saved : int
Number of saved images in the previous process. If the process is
resumed it is non-zero.
"""
res = Result(paths=paths_queue)
if previously_saved == 0:
_clean_dir(dir_name)
steps = len(paths_queue) + previously_saved
loader = ImageLoader()
while res.paths:
from_path, to_path = res.paths.popleft()
_save_an_image(loader, from_path, to_path, target_size)
state.set_progress_value((1 - len(res.paths) / steps) * 100)
state.set_partial_result(res)
if state.is_interruption_requested():
return res
return res
def run_mds(matrix: DistMatrix, max_iter: int, step_size: int, init_type: int,
embedding: np.ndarray, state: TaskState):
res = Result(embedding=embedding)
iterations_done = 0
init = embedding
state.set_status("Running...")
oldstress = np.finfo(np.float).max
while True:
step_iter = min(max_iter - iterations_done, step_size)
mds = MDS(
dissimilarity="precomputed", n_components=2,
n_init=1, max_iter=step_iter,
init_type=init_type, init_data=init
)
mdsfit = mds(matrix)
iterations_done += step_iter
embedding, stress = mdsfit.embedding_, mdsfit.stress_
emb_norm = np.sqrt(np.sum(embedding ** 2, axis=1)).sum()
if emb_norm > 0:
stress /= emb_norm
res.embedding = embedding
state.set_partial_result(res)
state.set_progress_value(100 * iterations_done / max_iter)
if iterations_done >= max_iter or stress == 0 or \
(oldstress - stress) < mds.params["eps"]:
return res
init = embedding
oldstress = stress
if state.is_interruption_requested():
return res
def worker(data: Table, learner, state: TaskState):
# No need to check for irregularities, this is done in widget
time_var, event_var = get_survival_endpoints(data.domain)
def fit_cox_models(attrs_combinations):
results = []
for attrs in attrs_combinations:
columns = attrs + [time_var.name, event_var.name]
cph_model = learner(data[:, columns])
log2p = cph_model.ll_ratio_log2p()
result = Result(log2p, cph_model)
results.append(result)
return results
attributes = [attr for attr in data.domain.attributes]
progress_steps = iter(np.linspace(0, 100, len(attributes)))
_trace = fit_cox_models([attributes])
while len(_trace) != len(data.domain.attributes):
attributes = [attr for attr in _trace[-1].model.domain.attributes]
if len(attributes) > 1:
combinations = [
list(comb)
for comb in itertools.combinations(attributes,
len(attributes) - 1)
]
else:
combinations = [attributes]
results = fit_cox_models(combinations)
_trace.append(max(results, key=lambda result: result.log2p))
state.set_progress_value(next(progress_steps))
return _trace
def run_vizrank(compute_score: Callable, iterate_states: Callable,
saved_state: Optional[Iterable], scores: List, progress: int,
state_count: int, task: TaskState):
task.set_status("Getting combinations...")
task.set_progress_value(0.1)
states = iterate_states(saved_state)
task.set_status("Getting scores...")
res = Result(queue=Queue(), scores=None)
scores = scores.copy()
can_set_partial_result = True
def do_work(st, next_st):
try:
score = compute_score(st)
if score is not None:
pos = bisect_left(scores, score)
res.queue.put_nowait(
QueuedScore(position=pos,
score=score,
state=st,
next_state=next_st))
scores.insert(pos, score)
except Exception: # ignore current state in case of any problem
pass
res.scores = scores.copy()
def reset_flag():
nonlocal can_set_partial_result
can_set_partial_result = True
state = None
next_state = next(states)
try:
while True:
if task.is_interruption_requested():
return res
task.set_progress_value(int(progress * 100 / max(1, state_count)))
progress += 1
state = copy.copy(next_state)
next_state = copy.copy(next(states))
do_work(state, next_state)
# for simple scores (e.g. correlations widget) and many feature
# combinations, the 'partial_result_ready' signal (emitted by
# invoking 'task.set_partial_result') was emitted too frequently
# for a longer period of time and therefore causing the widget
# being unresponsive
if can_set_partial_result:
task.set_partial_result(res)
can_set_partial_result = False
Timer(0.01, reset_flag).start()
except StopIteration:
do_work(state, None)
task.set_partial_result(res)
return res
def run(gene_sets: GeneSets, selected_gene_sets: List[Tuple[str, ...]], genes,
state: TaskState) -> Results:
results = Results()
items = []
step, steps = 0, len(gene_sets)
if not genes:
return results
state.set_status('Calculating...')
for gene_set in sorted(gene_sets):
step += 1
if step % (steps / 10) == 0:
state.set_progress_value(100 * step / steps)
if gene_set.hierarchy not in selected_gene_sets:
continue
if state.is_interruption_requested():
return results
matched_set = gene_set.genes & genes
if len(matched_set) > 0:
category_column = QStandardItem()
term_column = QStandardItem()
count_column = QStandardItem()
genes_column = QStandardItem()
category_column.setData(", ".join(gene_set.hierarchy),
Qt.DisplayRole)
term_column.setData(gene_set.name, Qt.DisplayRole)
term_column.setData(gene_set.name, Qt.ToolTipRole)
# there was some cases when link string was not empty string but not valid (e.g. "_")
if gene_set.link and urlparse(gene_set.link).scheme:
term_column.setData(gene_set.link, LinkRole)
term_column.setForeground(QColor(Qt.blue))
count_column.setData(matched_set, Qt.UserRole)
count_column.setData(len(matched_set), Qt.DisplayRole)
genes_column.setData(len(gene_set.genes), Qt.DisplayRole)
genes_column.setData(
set(gene_set.genes),
Qt.UserRole) # store genes to get then on output on selection
items.append(
[count_column, genes_column, category_column, term_column])
results.items = items
return results
def compute_scores(
data: Table,
genes: Table,
p_threshold: float,
p_value_fun: str,
scoring: str,
start: float,
end: float,
result: Result,
state: TaskState,
):
if not data or not genes:
result.scores.z_vals = None
result.scores.annotations = None
result.scores.p_vals = None
result.scores.table = None
else:
state.set_status("Computing scores...")
weights = np.array([15, 75, 10]) * (end - start) / 100
if not result.scores.z_vals:
result.scores.z_vals = AnnotateSamplesMeta.mann_whitney_test(
data)
state.set_partial_result(("scores", result))
state.set_progress_value(weights[0])
if state.is_interruption_requested():
return
if not result.scores.annotations or not result.scores.p_vals:
annot, p_vals = AnnotateSamplesMeta.assign_annotations(
result.scores.z_vals,
genes,
data,
p_value_fun=p_value_fun,
scoring=scoring)
result.scores.annotations = annot
result.scores.p_vals = p_vals
state.set_partial_result(("scores", result))
state.set_progress_value(weights[1])
if state.is_interruption_requested():
return
result.scores.table = AnnotateSamplesMeta.filter_annotations(
result.scores.annotations,
result.scores.p_vals,
p_threshold=p_threshold)
state.set_partial_result(("scores", result))
def worker(table: Table, covariates: List, time_var: str, event_var: str,
state: TaskState):
with multiprocessing.Manager() as _manager:
_queue = _manager.Queue()
_cpu_count = cpu_count()
df = table_to_frame(table, include_metas=False)
df = df.astype({event_var: np.float64})
if len(covariates) > 50:
batches = (df[[time_var, event_var] + batch] for batch in
[covariates[i::_cpu_count] for i in range(_cpu_count)])
else:
batches = (df[[time_var, event_var] + [cov]] for cov in covariates)
progress_steps = iter(np.linspace(0, 100, len(covariates)))
with multiprocessing.Pool(processes=_cpu_count) as pool:
results = pool.map_async(
partial(
batch_to_process,
_queue,
time_var,
event_var,
),
batches,
)
while True:
try:
state.set_progress_value(next(progress_steps))
_queue.get(timeout=3)
except (queue.Empty, StopIteration):
break
stacked_result = np.vstack(results.get())
covariate_names = stacked_result[:, 0]
results = stacked_result[:, 1:].astype(float)
_, pvals_corrected = fdrcorrection(results[:, -1], is_sorted=False)
results = np.hstack(
(results, pvals_corrected.reshape(pvals_corrected.shape[0],
-1)))
return covariate_names, results
def count_words(data: Corpus, state: TaskState) -> Tuple[Counter, bool]:
"""
This function implements counting process of the word cloud widget and
is called in the separate thread by concurrent.
Parameters
----------
data
Corpus with the data
state
State used to report status.
Returns
-------
Reports counts as a counter and boolean that tell whether the data were
retrieved on bag of words basis.
"""
state.set_status("Calculating...")
state.set_progress_value(0)
bow_counts = _bow_words(data)
state.set_progress_value(0.5)
if bow_counts:
corpus_counter = Counter(bow_counts)
else:
corpus_counter = Counter(w for doc in data.ngrams for w in doc)
state.set_progress_value(1)
return corpus_counter, bool(bow_counts)
def run(data: Table, embedding: Optional[np.ndarray], state: TaskState):
res = Result(embedding=embedding)
# simulate wasteful calculation (increase 'steps')
step, steps = 0, 10
state.set_status("Calculating...")
while step < steps:
for _ in range(steps):
x_data = np.array(np.mean(data.X, axis=1))
if x_data.ndim == 2:
x_data = x_data.ravel()
y_data = np.random.rand(len(x_data))
embedding = np.vstack((x_data, y_data)).T
step += 1
if step % (steps / 10) == 0:
state.set_progress_value(100 * step / steps)
if state.is_interruption_requested():
return res
res.embedding = embedding
state.set_partial_result(res)
return res
def run(data: Table, embedding: Optional[np.ndarray], state: TaskState):
res = Result(embedding=embedding)
# simulate wasteful calculation (increase 'steps')
step, steps = 0, 10
state.set_status("Calculating...")
while step < steps:
for _ in range(steps):
x_data = np.array(np.mean(data.X, axis=1))
if x_data.ndim == 2:
x_data = x_data.ravel()
y_data = np.random.rand(len(x_data))
embedding = np.vstack((x_data, y_data)).T
step += 1
if step % (steps / 10) == 0:
state.set_progress_value(100 * step / steps)
if state.is_interruption_requested():
return res
res.embedding = embedding
state.set_partial_result(res)
return res
def run_freeviz(data: Table, projector: FreeViz, state: TaskState):
res = Result(projector=projector, projection=None)
step, steps = 0, MAX_ITERATIONS
initial = res.projector.components_.T
state.set_status("Calculating...")
while True:
# Needs a copy because projection should not be modified inplace.
# If it is modified inplace, the widget and the thread hold a
# reference to the same object. When the thread is interrupted it
# is still modifying the object, but the widget receives it
# (the modified object) with a delay.
res.projection = res.projector(data).copy()
anchors = res.projector.components_.T
res.projector.initial = anchors
state.set_partial_result(res)
if np.allclose(initial, anchors, rtol=1e-5, atol=1e-4):
return res
initial = anchors
step += 1
state.set_progress_value(100 * step / steps)
if state.is_interruption_requested():
return res
def run_freeviz(data: Table, projector: FreeViz, state: TaskState):
res = Result(projector=projector, projection=None)
step, steps = 0, MAX_ITERATIONS
initial = res.projector.components_.T
state.set_status("Calculating...")
while True:
# Needs a copy because projection should not be modified inplace.
# If it is modified inplace, the widget and the thread hold a
# reference to the same object. When the thread is interrupted it
# is still modifying the object, but the widget receives it
# (the modified object) with a delay.
res.projection = res.projector(data).copy()
anchors = res.projector.components_.T
res.projector.initial = anchors
state.set_partial_result(res)
if np.allclose(initial, anchors, rtol=1e-5, atol=1e-4):
return res
initial = anchors
step += 1
state.set_progress_value(100 * step / steps)
if state.is_interruption_requested():
return res
def runner(self, state: TaskState) -> Table:
exp_type = self.data_output_options.expression_type[self.exp_type].type
exp_source = self.data_output_options.expression_sources[
self.exp_source]
proc_slug = self.data_output_options.process[self.proc_slug].slug
collection_id = self.selected_collection_id
table = self.data_table
progress_steps_download = iter(np.linspace(0, 50, 2))
def callback(i: float, status=""):
state.set_progress_value(i * 100)
if status:
state.set_status(status)
if state.is_interruption_requested():
raise Exception
if not table:
collection = self.res.get_collection_by_id(collection_id)
coll_table = resdk.tables.RNATables(
collection,
expression_source=exp_source,
expression_process_slug=proc_slug,
progress_callable=wrap_callback(callback, end=0.5),
)
species = coll_table._data[0].output['species']
sample = coll_table._samples[0]
state.set_status('Downloading ...')
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
df_exp = coll_table.exp if exp_type != 'rc' else coll_table.rc
df_exp = df_exp.rename(index=coll_table.readable_index)
df_metas = coll_table.meta
df_metas = df_metas.rename(index=coll_table.readable_index)
df_qc = None
if self.append_qc_data:
# TODO: check if there is a way to detect if collection
# table contains QC data
try:
df_qc = coll_table.qc
df_qc = df_qc.rename(index=coll_table.readable_index)
except ValueError:
pass
loop.close()
state.set_status('To data table ...')
duplicates = {
item
for item, count in Counter([
label.split('.')[1]
for label in df_metas.columns.to_list() if '.' in label
]).items() if count > 1
}
# what happens if there is more nested sections?
section_name_to_label = {
section['name']: section['label']
for section in sample.descriptor_schema.schema
}
column_labels = {}
for field_schema, fields, path in iterate_schema(
sample.descriptor, sample.descriptor_schema.schema,
path=''):
path = path[1:] # this is ugly, but cant go around it
if path not in df_metas.columns:
continue
label = field_schema['label']
section_name, field_name = path.split('.')
column_labels[path] = (
label if field_name not in duplicates else
f'{section_name_to_label[section_name]} - {label}')
df_exp = df_exp.reset_index(drop=True)
df_metas = df_metas.astype('object')
df_metas = df_metas.fillna(np.nan)
df_metas = df_metas.replace('nan', np.nan)
df_metas = df_metas.rename(columns=column_labels)
if df_qc is not None:
df_metas = pd.merge(df_metas,
df_qc,
left_index=True,
right_index=True)
xym, domain_metas = vars_from_df(df_metas)
x, _, m = xym
x_metas = np.hstack((x, m))
attrs = [ContinuousVariable(col) for col in df_exp.columns]
metas = domain_metas.attributes + domain_metas.metas
domain = Domain(attrs, metas=metas)
table = Table(domain, df_exp.to_numpy(), metas=x_metas)
state.set_progress_value(next(progress_steps_download))
state.set_status('Matching genes ...')
progress_steps_gm = iter(
np.linspace(50, 99, len(coll_table.gene_ids)))
def gm_callback():
state.set_progress_value(next(progress_steps_gm))
tax_id = species_name_to_taxid(species)
gm = GeneMatcher(tax_id, progress_callback=gm_callback)
table = gm.match_table_attributes(table, rename=True)
table.attributes[TableAnnotation.tax_id] = tax_id
table.attributes[TableAnnotation.gene_as_attr_name] = True
table.attributes[TableAnnotation.gene_id_attribute] = 'Entrez ID'
self.data_table = table
state.set_status('Normalizing ...')
table = self.normalize(table)
state.set_progress_value(100)
return table