def _update(self):
assert self.data is not None
# collect all learners for which results have not yet been computed
need_update = [(i, item) for (i, item) in enumerate(self.learners)
if item.results is None]
if not need_update:
return
learners = [item.learner for _, item in need_update]
if self.testdata is None:
# compute the learning curve result for all learners in one go
results = learning_curve(
learners,
self.data,
folds=self.folds,
proportions=self.curvePoints,
)
else:
results = learning_curve_with_test_data(
learners,
self.data,
self.testdata,
times=self.folds,
proportions=self.curvePoints,
)
# split the combined result into per learner/model results
results = [
list(Results.split_by_model(p_results)) for p_results in results
]
for i, (_, item) in enumerate(need_update):
item.results = [p_results[i] for p_results in results]
def _update(self):
assert self.data is not None
# collect all learners for which results have not yet been computed
need_update = [(id, learner) for id, learner in self.learners.items()
if self.results[id] is None]
if not need_update:
return
learners = [learner for _, learner in need_update]
self.progressBarInit()
if self.testdata is None:
# compute the learning curve result for all learners in one go
results = learning_curve(
learners,
self.data,
folds=self.folds,
proportions=self.curvePoints,
callback=lambda value: self.progressBarSet(100 * value))
else:
results = learning_curve_with_test_data(
learners,
self.data,
self.testdata,
times=self.folds,
proportions=self.curvePoints,
callback=lambda value: self.progressBarSet(100 * value))
self.progressBarFinished()
# split the combined result into per learner/model results
results = [
list(Results.split_by_model(p_results)) for p_results in results
]
for i, (id, learner) in enumerate(need_update):
self.results[id] = [p_results[i] for p_results in results]
def _update(self):
assert self.data is not None
# collect all learners for which results have not yet been computed
need_update = [(id, learner) for id, learner in self.learners.items()
if self.results[id] is None]
if not need_update:
return
learners = [learner for _, learner in need_update]
if self.testdata is None:
# compute the learning curve result for all learners in one go
results = learning_curve(
learners, self.data, folds=self.folds,
proportions=self.curvePoints,
)
else:
results = learning_curve_with_test_data(
learners, self.data, self.testdata, times=self.folds,
proportions=self.curvePoints,
)
# split the combined result into per learner/model results
results = [list(Results.split_by_model(p_results)) for p_results in results]
for i, (id, learner) in enumerate(need_update):
self.results[id] = [p_results[i] for p_results in results]
def _update(self):
assert self.data is not None
# collect all learners for which results have not yet been computed
need_update = [(id, learner) for id, learner in self.learners.items()
if self.results[id] is None]
if not need_update: #啥意思？
return
learners = [learner for _, learner in need_update]
# compute the learning curve result for all learners in one go
results = learning_curve(
learners,
self.data,
folds=self.folds,
proportions=self.curvePoints,
) #这是一个元组
# split the combined result into per learner/model results
results = [
list(Results.split_by_model(p_results)) for p_results in results
]
for i, (id, learner) in enumerate(need_update):
self.results[id] = [p_results[i] for p_results in results]
def _task_finished(self, f):
"""
Parameters
----------
f : Future
The future instance holding the result of learner evaluation.
"""
assert self.thread() is QThread.currentThread()
assert self._task is not None
assert self._task.future is f
assert f.done()
self._task = None
self.progressBarFinished()
try:
results = f.result() # type: List[Results]
except Exception as ex:
# Log the exception with a traceback
log = logging.getLogger()
log.exception(__name__, exc_info=True)
self.error("Exception occurred during evaluation: {!r}".format(ex))
# clear all results
for key in self.results.keys():
self.results[key] = None
else:
# split the combined result into per learner/model results ...
results = [
list(Results.split_by_model(p_results))
for p_results in results
] # type: List[List[Results]]
assert all(len(r.learners) == 1 for r1 in results for r in r1)
assert len(results) == len(self.curvePoints)
learners = [r.learners[0] for r in results[0]]
learner_id = {
learner: id_
for id_, learner in self.learners.items()
}
# ... and update self.results
for i, learner in enumerate(learners):
id_ = learner_id[learner]
self.results[id_] = [p_results[i] for p_results in results]
# [end-snippet-9]
# update the display
self._update_curve_points()
self._update_table()
def _task_finished(self, f):
"""
Parameters
----------
f : Future
The future instance holding the result of learner evaluation.
"""
assert self.thread() is QThread.currentThread()
assert self._task is not None
assert self._task.future is f
assert f.done()
self._task = None
self.progressBarFinished()
try:
results = f.result() # type: List[Results]
except Exception as ex:
# Log the exception with a traceback
log = logging.getLogger()
log.exception(__name__, exc_info=True)
self.error("Exception occurred during evaluation: {!r}"
.format(ex))
# clear all results
for key in self.results.keys():
self.results[key] = None
else:
# split the combined result into per learner/model results ...
results = [list(Results.split_by_model(p_results))
for p_results in results] # type: List[List[Results]]
assert all(len(r.learners) == 1 for r1 in results for r in r1)
assert len(results) == len(self.curvePoints)
learners = [r.learners[0] for r in results[0]]
learner_id = {learner: id_ for id_, learner in self.learners.items()}
# ... and update self.results
for i, learner in enumerate(learners):
id_ = learner_id[learner]
self.results[id_] = [p_results[i] for p_results in results]
# [end-snippet-9]
# update the display
self._update_curve_points()
self._update_table()
def _update(self):
assert self.data is not None
# collect all learners for which results have not yet been computed
need_update = [(id, learner) for id, learner in self.learners.items()
if self.results[id] is None]
if not need_update:
return
learners = [learner for _, learner in need_update]
self.progressBarInit()
# compute the learning curve result for all learners in one go
results = learning_curve(
learners, self.data, folds=self.folds,
proportions=self.curvePoints,
callback=lambda value: self.progressBarSet(100 * value)
)
self.progressBarFinished()
# split the combined result into per learner/model results
results = [list(Results.split_by_model(p_results))
for p_results in results]
for i, (id, learner) in enumerate(need_update):
self.results[id] = [p_results[i] for p_results in results]