def test_failure(self):
# Not enough elements for the placeholders.
self.assertEqual(format_status_text(["%s"]), "N/A")
self.assertEqual(format_status_text(["%s"], self._tr), "N/E")
# No elements at all.
self.assertEqual(format_status_text([]), "N/A")
self.assertEqual(format_status_text([], self._tr), "N/E")
def test_insuccess(self):
# Not enough elements for the placeholders.
self.assertEqual(format_status_text(["%s"]), "N/A")
self.assertEqual(format_status_text(["%s"], self._tr), "N/E")
# No elements at all.
self.assertEqual(format_status_text([]), "N/A")
self.assertEqual(format_status_text([], self._tr), "N/E")
def test_success_with_translator(self):
self.assertEqual(format_status_text([""], self._tr), "")
self.assertEqual(format_status_text(["ASD"], self._tr), "ESD")
# Translation is applied before formatting.
self.assertEqual(format_status_text(["A%s", "ASD"], self._tr), "EASD")
self.assertEqual(
format_status_text(["AAA %s\n%s", "AAA", "AE"], self._tr),
"EEE AAA\nAE")
def test_success_with_translator(self):
self.assertEqual(format_status_text([""], self._tr), "")
self.assertEqual(format_status_text(["ASD"], self._tr), "ESD")
# Translation is applied before formatting.
self.assertEqual(format_status_text(["A%s", "ASD"], self._tr), "EASD")
self.assertEqual(
format_status_text(["AAA %s\n%s", "AAA", "AE"], self._tr),
"EEE AAA\nAE")
def test_testcases(base_dir, solution, language, assume=None):
global task, file_cacher
# Use a FileCacher with a NullBackend in order to avoid to fill
# the database with junk
if file_cacher is None:
file_cacher = FileCacher(null=True)
cmscontrib.loaders.italy_yaml.logger = NullLogger()
# Load the task
# TODO - This implies copying a lot of data to the FileCacher,
# which is annoying if you have to do it continuously; it would be
# better to use a persistent cache (although local, possibly
# filesystem-based instead of database-based) and somehow detect
# when the task has already been loaded
if task is None:
loader = cmscontrib.loaders.italy_yaml.YamlLoader(
base_dir, file_cacher)
task = loader.get_task(get_statement=False)
# Prepare the EvaluationJob
dataset = task.active_dataset
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, solution),
"Solution %s for task %s" % (solution, task.name))
executables = {task.name: Executable(filename=task.name, digest=digest)}
jobs = [
(t,
EvaluationJob(
operation=ESOperation(ESOperation.EVALUATION, None, dataset.id,
dataset.testcases[t].codename).to_dict(),
language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases
]
tasktype = get_task_type(dataset=dataset)
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0])
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
move_cursor(directions.UP, erase=True)
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
status = job.plus.get("exit_status")
info.append(
(job.plus.get("execution_time"), job.plus.get("execution_memory")))
points.append(float(job.outcome))
# Avoid printing unneeded newline
job.text = [t.rstrip() for t in job.text]
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print("Want to stop and consider everything to timeout? [y/N] ",
end='')
sys.stdout.flush()
if assume is not None:
tmp = assume
print(tmp)
else:
# User input with a timeout of 5 seconds, at the end of which
# we automatically say "n". ready will be a list of input ready
# for reading, or an empty list if the timeout expired.
# See: http://stackoverflow.com/a/2904057
ready, _, _ = select.select([sys.stdin], [], [], 5)
if ready:
tmp = sys.stdin.readline().strip().lower()
else:
tmp = 'n'
print(tmp)
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
print()
move_cursor(directions.UP, erase=True)
# Subtasks scoring
subtasks = json.loads(dataset.score_type_parameters)
if not isinstance(subtasks, list) or len(subtasks) == 0:
subtasks = [[100, len(info)]]
if dataset.score_type == 'GroupMin':
scoreFun = min
else:
if dataset.score_type != 'Sum':
logger.warning("Score type %s not yet supported! Using Sum" %
dataset.score_type)
def scoreFun(x):
return sum(x) / len(x)
pos = 0
sts = []
# For each subtask generate a list of testcase it owns, the score gained
# and the highest time and memory usage.
for i in subtasks:
stscores = []
stsdata = []
worst = [0, 0]
try:
for _ in xrange(i[1]):
stscores.append(points[pos])
stsdata.append(
(tcnames[pos], points[pos], comments[pos], info[pos]))
if info[pos][0] > worst[0]:
worst[0] = info[pos][0]
if info[pos][1] > worst[1]:
worst[1] = info[pos][1]
pos += 1
sts.append((scoreFun(stscores) * i[0], i[0], stsdata, worst))
except:
sts.append((0, i[0], stsdata, [0, 0]))
# Result pretty printing
# Strips sol/ and _EVAL from the solution's name
solution = solution[4:-5]
print()
clen = max(len(c) for c in comments)
for st, d in enumerate(sts):
print(
"Subtask %d:" % st,
add_color_to_string(
"%5.2f/%d" % (d[0], d[1]),
colors.RED if abs(d[0] - d[1]) > 0.01 else colors.GREEN,
bold=True))
for (i, p, c, w) in d[2]:
print("%s)" % i,
add_color_to_string(
"%5.2lf" % p,
colors.RED if abs(p - 1) > 0.01 else colors.BLACK),
"--- %s [Time:" % c.ljust(clen),
add_color_to_string(
("%5.3f" % w[0]) if w[0] is not None else "N/A",
colors.BLUE if w[0] is not None
and w[0] >= 0.95 * d[3][0] else colors.BLACK),
"Memory:",
add_color_to_string(
"%5s" % mem_human(w[1]) if w[1] is not None else "N/A",
colors.BLUE if w[1] is not None
and w[1] >= 0.95 * d[3][1] else colors.BLACK,
),
end="]")
move_cursor(directions.RIGHT, 1000)
move_cursor(directions.LEFT, len(solution) - 1)
print(add_color_to_string(solution, colors.BLACK, bold=True))
print()
sols.append((solution, sum([st[0] for st in sts])))
global tested_something
if not tested_something:
tested_something = True
atexit.register(print_at_exit)
return zip(points, comments, info)
def wrapped_format_status_text(ctx, status_text):
translation = ctx.get("translation", DEFAULT_TRANSLATION)
return format_status_text(status_text, translation=translation)
def wrapped_format_status_text(ctx, status_text):
translation = ctx.get("translation", DEFAULT_TRANSLATION)
return format_status_text(status_text, translation=translation)
def get(self, submission_id):
"""Retrieve a single submission.
Query the database for the submission with the given ID, and
the dataset given as query parameter (or the active one).
submission_id (int): the ID of a submission.
"""
# If it's not an integer we will ignore it. But if it's an
# integer of a dataset that doesn't exist we'll raise a 404.
dataset_id = local.request.args.get("dataset_id", type=int)
with SessionGen() as local.session:
# Load the submission, and check for existence.
submission = Submission.get_from_id(submission_id, local.session)
if submission is None:
raise NotFound()
# Load the dataset.
if dataset_id is not None:
dataset = Dataset.get_from_id(dataset_id, local.session)
if dataset is None:
raise NotFound()
else:
q = local.session.query(Dataset)
q = q.join(Task, Dataset.id == Task.active_dataset_id)
q = q.filter(Task.id == submission.task_id)
dataset = q.one()
# Get the result (will fire a query).
submission_result = submission.get_result(dataset)
# Get the ScoreType (will fire a query for testcases).
score_type = get_score_type(dataset=dataset)
# Produce the data structure.
s = submission
sr = submission_result
result = {
'_ref': "%s" % s.id,
'dataset': '%s' % dataset.id,
'user': "******" % s.user_id,
'task': "%s" % s.task_id,
'timestamp': make_timestamp(s.timestamp),
'language': s.language,
# No files, no token: AWS doesn't need them.
}
if sr is not None:
result.update({
'compilation_outcome':
{"ok": True,
"fail": False}.get(sr.compilation_outcome),
'compilation_text':
format_status_text(sr.compilation_text),
'compilation_tries': sr.compilation_tries,
'compilation_stdout': sr.compilation_stdout,
'compilation_stderr': sr.compilation_stderr,
'compilation_time': sr.compilation_time,
'compilation_wall_clock_time':
sr.compilation_wall_clock_time,
'compilation_memory': sr.compilation_memory,
'compilation_shard': sr.compilation_shard,
'compilation_sandbox': sr.compilation_sandbox,
'evaluation_outcome':
{"ok": True}.get(sr.evaluation_outcome),
'evaluation_tries': sr.evaluation_tries,
'evaluations': dict((ev.codename, {
'codename': ev.codename,
'outcome': ev.outcome,
'text': format_status_text(ev.text),
'execution_time': ev.execution_time,
'execution_wall_clock_time':
ev.execution_wall_clock_time,
'execution_memory': ev.execution_memory,
'evaluation_shard': ev.evaluation_shard,
'evaluation_sandbox': ev.evaluation_sandbox,
}) for ev in sr.evaluations),
'score': sr.score,
'max_score': score_type.max_score,
'score_details':
score_type.get_html_details(sr.score_details)
if sr.score is not None else None,
})
else:
# Just copy all fields with None.
result.update({
'compilation_outcome': None,
'compilation_text': None,
'compilation_tries': 0,
'compilation_stdout': None,
'compilation_stderr': None,
'compilation_time': None,
'compilation_wall_clock_time': None,
'compilation_memory': None,
'compilation_shard': None,
'compilation_sandbox': None,
'evaluation_outcome': None,
'evaluation_tries': 0,
'evaluations': {},
'score': None,
'max_score': score_type.max_score,
'score_details': None,
})
# Encode and send.
local.response.mimetype = "application/json"
local.response.data = json.dumps(result)
def compute_unit_test_score(self, submission_result,
submission_info):
"""Compute the score of a unit test.
Format of the returned details:
unit_test: True/False
subtasks:
name: name of the subtask
status: (0, "okay")
groups:
verdict: (42, "")
cases:
line: (,) case_line()
verdict: (42, "No expl. exp.") judge_case()
if len(mandatory) != 0
time: 0.412
memory: 33659290 in bytes
"""
if submission_info is None:
return {"unit_test": True,
"verdict": (-1, "Not a Unit Test")}
submission_info = json.loads(submission_info)
expected_sample_score = submission_info["expected_sample_score"]
expected_partial_feedback_score = submission_info["expected_partial_feedback_score"]
expected_final_score = submission_info["expected_final_score"]
expected_sample_score_info = submission_info["expected_sample_score_info"]
expected_partial_feedback_score_info = submission_info["expected_partial_feedback_score_info"]
expected_final_score_info = submission_info["expected_final_score_info"]
expectations = {tuple(json.loads(key)): val for key, val
in iteritems(submission_info["expected"])}
case_expectations = submission_info["expected_case"]
possible_task = expectations[()]
# Actually, this means it didn't even compile
if not submission_result.evaluated():
subtasks_failed = True
subtasks = []
else:
evaluations = dict((ev.codename, ev)
for ev in submission_result.evaluations)
subtasks = []
subtasks_failed = False
for subtask in self.parameters["subtasks"]:
subtasks.append({
"name": subtask["name"],
"groups": []
})
possible_subtask = expectations[tuple(subtask["key"])]
worst_group = (1, "okay")
group_status = []
for i, g in enumerate(subtask["groups"]):
possible_group = expectations[tuple(g["key"])]
possible = possible_task + possible_subtask + possible_group
subtasks[-1]["groups"].append({"verdict": (42, ""),
"cases": []})
min_f = 1.0 # Minimum "score" of a test case in this group
cases_failed = False
# List of all results of all test cases in this group
case_results = []
extended_results = []
for testcase in g["testcases"]:
idx = testcase["codename"]
r = UnitTest.get_result(submission_info["limits"],
evaluations[idx])
min_f = min(min_f, float(evaluations[idx].outcome))
mandatory = case_expectations[idx]
l = UnitTest.case_line(r, mandatory, possible)
v = (42, "No case-specific expectations.")
# Test case expectations
if len(mandatory) != 0:
accepted, desc = v = \
UnitTest.judge_case(r, mandatory, possible)
if accepted <= 0:
cases_failed = True
extended_results += r
case_results += \
[x for x in r if not UnitTest.ignore(x, mandatory)
and x not in mandatory]
else:
case_results += r
v = (v[0],
v[1] + "\nGrader output: " +
format_status_text((evaluations[idx].text)).strip())
subtasks[-1]["groups"][-1]["cases"].\
append({"line": l, "verdict": v,
"time": evaluations[idx].execution_time,
"memory": evaluations[idx].execution_memory})
status, short, desc = \
UnitTest.judge_group(case_results, extended_results,
possible, [])
if cases_failed:
if status > 0:
desc = ""
else:
desc += "\n\n"
status = -1
desc += "At least one testcase did not behave as " \
"expected, cf. the \"test verdict\" column."
short = "failed"
subtasks[-1]["groups"][-1]["verdict"] = (status, desc)
worst_group = min(worst_group, (status, short))
group_status.append(status)
subtasks[-1]["status"] = (worst_group[0], worst_group[1].upper())
if all(s == 1337 for s in group_status):
subtasks[-1]["status"] = (1337, "IGNORED")
elif subtasks[-1]["status"][0] > 0 and any(s == 1337
for s in group_status):
subtasks[-1]["status"] = (1337, "PARTIALLY IGNORED")
if len(group_status) == 0:
subtasks[-1]["status"] = (1337, "EMPTY")
if subtasks[-1]["status"][0] <= 0:
subtasks_failed = True
def is_in(x, l):
return l[0] <= x <= l[1]
sample_score = self._compute_score(submission_result, "sample")[0]
partial_feedback_score = self._compute_score(submission_result, "partial")[0]
final_score = self._compute_score(submission_result, "final")[0]
sample_score_okay = is_in(sample_score, expected_sample_score)
partial_feedback_score_okay = is_in(partial_feedback_score, expected_partial_feedback_score)
final_score_okay = is_in(final_score, expected_final_score)
partial_feedback_enabled = self.parameters["feedback"] == "partial"
okay = not subtasks_failed \
and sample_score_okay \
and (partial_feedback_score_okay or not partial_feedback_enabled) \
and final_score_okay \
details = {
"unit_test": True,
"subtasks": subtasks,
"verdict": (1, "Okay") if okay else (0, "Failed"),
"sample_score_okay": sample_score_okay,
"sample_score": sample_score,
"expected_sample_score": expected_sample_score_info,
"partial_feedback_enabled": partial_feedback_enabled,
"partial_feedback_score_okay": partial_feedback_score_okay,
"partial_feedback_score": partial_feedback_score,
"expected_partial_feedback_score": expected_partial_feedback_score_info,
"final_score_okay": final_score_okay,
"final_score": final_score,
"expected_final_score": expected_final_score_info,
}
return details
def test_success_with_placeholders(self):
self.assertEqual(format_status_text(["%s", "ASD"]), "ASD")
self.assertEqual(format_status_text(["ASD%s\n%s", "QWE", "123"]),
"ASDQWE\n123")
def test_success_no_placeholders(self):
self.assertEqual(format_status_text([]), "N/A")
self.assertEqual(format_status_text([""]), "")
self.assertEqual(format_status_text(["ASD"]), "ASD")
self.assertEqual(format_status_text(["你好"]), "你好")
def test_testcases(base_dir, soluzione, language, assume=None):
global task, file_cacher
# Use a FileCacher with a NullBackend in order to avoid to fill
# the database with junk
if file_cacher is None:
file_cacher = FileCacher(null=True)
# Load the task
# TODO - This implies copying a lot of data to the FileCacher,
# which is annoying if you have to do it continuously; it would be
# better to use a persistent cache (although local, possibly
# filesystem-based instead of database-based) and somehow detect
# when the task has already been loaded
if task is None:
loader = YamlLoader(
os.path.realpath(os.path.join(base_dir, "..")),
file_cacher)
# Normally we should import the contest before, but YamlLoader
# accepts get_task() even without previous get_contest() calls
task = loader.get_task(os.path.split(os.path.realpath(base_dir))[1])
# Prepare the EvaluationJob
dataset = task.active_dataset
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, soluzione),
"Solution %s for task %s" % (soluzione, task.name))
executables = {task.name: Executable(filename=task.name, digest=digest)}
jobs = [(t, EvaluationJob(
language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input, output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases]
tasktype = get_task_type(dataset=dataset)
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0], end='')
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
status = job.plus["exit_status"]
info.append("Time: %5.3f Wall: %5.3f Memory: %s" %
(job.plus["execution_time"],
job.plus["execution_wall_clock_time"],
mem_human(job.plus["execution_memory"])))
points.append(float(job.outcome))
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print()
print("Want to stop and consider everything to timeout? [y/N]",
end='')
if assume is not None:
print(assume)
tmp = assume
else:
tmp = raw_input().lower()
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
# Result pretty printing
print()
clen = max(len(c) for c in comments)
ilen = max(len(i) for i in info)
for (i, p, c, b) in zip(tcnames, points, comments, info):
print("%s) %5.2lf --- %s [%s]" % (i, p, c.ljust(clen), b.center(ilen)))
return zip(points, comments, info)
def compute_unit_test_score(self, submission_result, submission_info):
"""Compute the score of a unit test.
Format of the returned details:
unit_test: True/False
subtasks:
name: name of the subtask
status: (0, "okay")
groups:
verdict: (42, "")
max_runtime: 0.412
max_memory: 33659290 in bytes
cases:
line: (,) case_line()
verdict: (42, "No expl. exp.") judge_case()
if len(mandatory) != 0
time: 0.412
memory: 33659290 in bytes
"""
eps = SubtaskGroup.EPS
threshold_lax = SubtaskGroup.THRESHOLD_LAX
threshold_strict = SubtaskGroup.THRESHOLD_STRICT
threshold_very_strict = SubtaskGroup.THRESHOLD_VERY_STRICT
if submission_info is None:
return {"unit_test": True, "verdict": (-1, "Not a Unit Test")}
submission_info = json.loads(submission_info)
expected_sample_score = submission_info["expected_sample_score"]
expected_partial_feedback_score = submission_info[
"expected_partial_feedback_score"]
expected_final_score = submission_info["expected_final_score"]
expected_sample_score_info = submission_info[
"expected_sample_score_info"]
expected_partial_feedback_score_info = submission_info[
"expected_partial_feedback_score_info"]
expected_final_score_info = submission_info[
"expected_final_score_info"]
expectations = {
tuple(json.loads(key)): val
for key, val in iteritems(submission_info["expected"])
}
case_expectations = submission_info["expected_case"]
possible_task = expectations[()]
useful = set()
essential = set()
all_cases = {
c["codename"]
for s in self.parameters["subtasks"] for g in s["groups"]
for c in g["testcases"]
}
dominated = {d: {c for c in all_cases if c != d} for d in all_cases}
# Actually, this means it didn't even compile
if not submission_result.evaluated():
subtasks_failed = True
subtasks = []
else:
evaluations = dict(
(ev.codename, ev) for ev in submission_result.evaluations)
subtasks = []
subtasks_failed = False
for subtask in self.parameters["subtasks"]:
subtasks.append({"name": subtask["name"], "groups": []})
possible_subtask = expectations[tuple(subtask["key"])]
worst_group = (1, "okay")
group_status = []
curr_group_dict = {}
for i, g in enumerate(subtask["groups"]):
"""
Check unit test
"""
possible_group = expectations[tuple(g["key"])]
possible = possible_task + possible_subtask + possible_group
subtasks[-1]["groups"].append({
"verdict": (42, ""),
"cases": []
})
min_f = 1.0 # Minimum "score" of a test case in this group
cases_failed = False
# List of all results of all test cases in this group
case_results = []
extended_results = []
for testcase in g["testcases"]:
idx = testcase["codename"]
r = UnitTest.get_result(submission_info["limits"],
evaluations[idx])
this_score = float(evaluations[idx].outcome)
curr_group_dict[idx] = this_score
min_f = min(min_f, this_score)
mandatory = case_expectations[idx]
l = UnitTest.case_line(r, mandatory, possible)
v = (42, "No case-specific expectations.")
# Test case expectations
if len(mandatory) != 0:
accepted, desc = v = \
UnitTest.judge_case(r, mandatory, possible)
if accepted <= 0:
cases_failed = True
extended_results += r
case_results += \
[x for x in r if not UnitTest.ignore(x, mandatory)
and x not in mandatory]
else:
case_results += r
v = (v[0],
v[1] + "\nGrader output: " + format_status_text(
(evaluations[idx].text)).strip())
subtasks[-1]["groups"][-1]["cases"].\
append({"line": l, "verdict": v,
"time": evaluations[idx].execution_time,
"memory": evaluations[idx].execution_memory,
"codename": idx})
status, short, desc = \
UnitTest.judge_group(case_results, extended_results,
possible, [])
if cases_failed:
if status > 0:
desc = ""
else:
desc += "\n\n"
status = -1
desc += "At least one testcase did not behave as " \
"expected, cf. the \"test verdict\" column."
short = "failed"
subtasks[-1]["groups"][-1]["verdict"] = (status, desc)
subtasks[-1]["groups"][-1]["max_runtime"] = \
max((c["time"]
for c in subtasks[-1]["groups"][-1]["cases"]),
default=None)
subtasks[-1]["groups"][-1]["max_memory"] = \
max((c["memory"]
for c in subtasks[-1]["groups"][-1]["cases"]),
default=None)
worst_group = min(worst_group, (status, short))
group_status.append(status)
"""
Check testcase utility
"""
if len(g["testcases"]) == 0 or subtask["sample"]:
continue
group_scores = sorted(curr_group_dict.values())
for c in g["testcases"]:
id = c["codename"]
s = curr_group_dict[id]
is_useful = (s <= min(
(1 + threshold_lax) * group_scores[0] + eps,
1 - eps))
is_essential = (
len(group_scores) == 1 or
(1 + threshold_strict) * s + eps < group_scores[1])
if is_essential:
essential.add(id)
elif is_useful:
useful.add(id)
dominated[id] &= {
c
for c, p in curr_group_dict.items()
if p <= (1 + threshold_very_strict) * s + eps
}
subtasks[-1]["status"] = (worst_group[0],
worst_group[1].upper())
if all(s == 1337 for s in group_status):
subtasks[-1]["status"] = (1337, "IGNORED")
elif subtasks[-1]["status"][0] > 0 and any(
s == 1337 for s in group_status):
subtasks[-1]["status"] = (1337, "PARTIALLY IGNORED")
if len(group_status) == 0:
subtasks[-1]["status"] = (1337, "EMPTY")
if subtasks[-1]["status"][0] <= 0:
subtasks_failed = True
score_precision = submission_info["score_precision"]
def is_in(x, l):
return round(l[0], score_precision) <= round(x, score_precision) \
<= round(l[1], score_precision)
sample_score = self._compute_score(submission_result, "sample")[0]
partial_feedback_score = self._compute_score(submission_result,
"partial")[0]
final_score = self._compute_score(submission_result, "final")[0]
sample_score_okay = is_in(sample_score, expected_sample_score)
partial_feedback_score_okay = is_in(partial_feedback_score,
expected_partial_feedback_score)
final_score_okay = is_in(final_score, expected_final_score)
partial_feedback_enabled = self.parameters["feedback"] == "partial"
okay = not subtasks_failed \
and sample_score_okay \
and (partial_feedback_score_okay or not partial_feedback_enabled) \
and final_score_okay \
details = {
"unit_test": True,
"subtasks": subtasks,
"verdict": (1, "Okay") if okay else (0, "Failed"),
"sample_score_okay": sample_score_okay,
"sample_score": sample_score,
"expected_sample_score": expected_sample_score_info,
"partial_feedback_enabled": partial_feedback_enabled,
"partial_feedback_score_okay": partial_feedback_score_okay,
"partial_feedback_score": partial_feedback_score,
"expected_partial_feedback_score":
expected_partial_feedback_score_info,
"final_score_okay": final_score_okay,
"final_score": final_score,
"expected_final_score": expected_final_score_info,
"dominated": dominated,
"essential": essential,
"useful": useful
}
return details
def test_testcases(base_dir, soluzione, language, assume=None):
global task, file_cacher
# Use a disabled FileCacher with a FSBackend in order to avoid to fill
# the database with junk and to save up space.
if file_cacher is None:
file_cacher = FileCacher(path=os.path.join(config.cache_dir,
'cmsMake'),
enabled=False)
# Load the task
if task is None:
loader = YamlLoader(os.path.realpath(os.path.join(base_dir, "..")),
file_cacher)
# Normally we should import the contest before, but YamlLoader
# accepts get_task() even without previous get_contest() calls
task = loader.get_task(os.path.split(os.path.realpath(base_dir))[1])
# Prepare the EvaluationJob
dataset = task.active_dataset
if dataset.task_type != "OutputOnly":
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, soluzione),
"Solution %s for task %s" % (soluzione, task.name))
executables = {
task.name: Executable(filename=task.name, digest=digest)
}
jobs = [(t,
EvaluationJob(language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(
dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit))
for t in dataset.testcases]
tasktype = get_task_type(dataset=dataset)
else:
print("Generating outputs...", end='')
files = {}
for t in sorted(dataset.testcases.keys()):
with file_cacher.get_file(dataset.testcases[t].input) as fin:
with TemporaryFile() as fout:
print(str(t), end='')
call(soluzione, stdin=fin, stdout=fout, cwd=base_dir)
fout.seek(0)
digest = file_cacher.put_file_from_fobj(fout)
outname = "output_%s.txt" % t
files[outname] = File(filename=outname, digest=digest)
jobs = [(t,
EvaluationJob(task_type=dataset.task_type,
task_type_parameters=json.loads(
dataset.task_type_parameters),
managers=dict(dataset.managers),
files=files,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit))
for t in dataset.testcases]
for k, job in jobs:
job._key = k
tasktype = get_task_type(dataset=dataset)
print()
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0], end='')
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
if dataset.task_type != "OutputOnly":
status = job.plus["exit_status"]
info.append("Time: %5.3f Wall: %5.3f Memory: %s" %
(job.plus["execution_time"],
job.plus["execution_wall_clock_time"],
mem_human(job.plus["execution_memory"])))
else:
status = "ok"
info.append("N/A")
points.append(float(job.outcome))
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print()
print("Want to stop and consider everything to timeout? [y/N]",
end='')
if assume is not None:
print(assume)
tmp = assume
else:
tmp = raw_input().lower()
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
# Result pretty printing
print()
clen = max(len(c) for c in comments)
ilen = max(len(i) for i in info)
for (i, p, c, b) in zip(tcnames, points, comments, info):
print("%s) %5.2lf --- %s [%s]" % (i, p, c.ljust(clen), b.center(ilen)))
return zip(points, comments, info)
def test_success_no_placeholders(self):
self.assertEqual(format_status_text([]), "N/A")
self.assertEqual(format_status_text([""]), "")
self.assertEqual(format_status_text(["ASD"]), "ASD")
self.assertEqual(format_status_text(["你好"]), "你好")
def test_testcases(base_dir, solution, language, assume=None):
global task, file_cacher
# Use a FileCacher with a NullBackend in order to avoid to fill
# the database with junk
if file_cacher is None:
file_cacher = FileCacher(null=True)
cmscontrib.loaders.italy_yaml.logger = NullLogger()
# Load the task
# TODO - This implies copying a lot of data to the FileCacher,
# which is annoying if you have to do it continuously; it would be
# better to use a persistent cache (although local, possibly
# filesystem-based instead of database-based) and somehow detect
# when the task has already been loaded
if task is None:
loader = cmscontrib.loaders.italy_yaml.YamlLoader(base_dir,
file_cacher)
task = loader.get_task(get_statement=False)
# Prepare the EvaluationJob
dataset = task.active_dataset
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, solution),
"Solution %s for task %s" % (solution, task.name))
executables = {task.name: Executable(filename=task.name, digest=digest)}
jobs = [(t, EvaluationJob(
language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input, output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases]
tasktype = get_task_type(dataset=dataset)
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0])
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
move_cursor(directions.UP, erase=True)
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
status = job.plus.get("exit_status")
info.append((job.plus.get("execution_time"),
job.plus.get("execution_memory")))
points.append(float(job.outcome))
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print("Want to stop and consider everything to timeout? [y/N]",
end='')
if assume is not None:
print(assume)
tmp = assume
else:
tmp = raw_input().lower()
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
print()
move_cursor(directions.UP, erase=True)
# Subtasks scoring
try:
subtasks = json.loads(dataset.score_type_parameters)
subtasks[0]
except:
subtasks = [[100, len(info)]]
if dataset.score_type == 'GroupMin':
scoreFun = min
else:
if dataset.score_type != 'Sum':
logger.warning("Score type %s not yet supported! Using Sum"
% dataset.score_type)
def scoreFun(x):
return sum(x) / len(x)
pos = 0
sts = []
# For each subtask generate a list of testcase it owns, the score gained
# and the highest time and memory usage.
for i in subtasks:
stscores = []
stsdata = []
worst = [0, 0]
try:
for _ in xrange(i[1]):
stscores.append(points[pos])
stsdata.append((tcnames[pos], points[pos],
comments[pos], info[pos]))
if info[pos][0] > worst[0]:
worst[0] = info[pos][0]
if info[pos][1] > worst[1]:
worst[1] = info[pos][1]
pos += 1
sts.append((scoreFun(stscores) * i[0], i[0], stsdata, worst))
except:
sts.append((0, i[0], stsdata, [0, 0]))
# Result pretty printing
# Strips sol/ and _EVAL from the solution's name
solution = solution[4:-5]
print()
clen = max(len(c) for c in comments)
for st, d in enumerate(sts):
print(
"Subtask %d:" % st,
add_color_to_string(
"%5.2f/%d" % (d[0], d[1]),
colors.RED if abs(d[0] - d[1]) > 0.01 else colors.GREEN,
bold=True
)
)
for (i, p, c, w) in d[2]:
print(
"%s)" % i,
add_color_to_string(
"%5.2lf" % p,
colors.RED if abs(p - 1) > 0.01 else colors.BLACK
),
"--- %s [Time:" % c.ljust(clen),
add_color_to_string(
("%5.3f" % w[0]) if w[0] is not None else "N/A",
colors.BLUE if w[0] is not None and w[0] >= 0.95 * d[3][0]
else colors.BLACK
),
"Memory:",
add_color_to_string(
"%5s" % mem_human(w[1]) if w[1] is not None else "N/A",
colors.BLUE if w[1] is not None and w[1] >= 0.95 * d[3][1]
else colors.BLACK,
),
end="]"
)
move_cursor(directions.RIGHT, 1000)
move_cursor(directions.LEFT, len(solution) - 1)
print(add_color_to_string(solution, colors.BLACK, bold=True))
print()
sols.append((solution, sum([st[0] for st in sts])))
global tested_something
if not tested_something:
tested_something = True
atexit.register(print_at_exit)
return zip(points, comments, info)
def test_success_with_placeholders(self):
self.assertEqual(format_status_text(["%s", "ASD"]), "ASD")
self.assertEqual(format_status_text(["ASD%s\n%s", "QWE", "123"]),
"ASDQWE\n123")
def test_testcases(base_dir, soluzione, assume=None):
global task, file_cacher
# Use a FileCacher with a NullBackend in order to avoid to fill
# the database with junk
if file_cacher is None:
file_cacher = FileCacher(null=True)
# Load the task
# TODO - This implies copying a lot of data to the FileCacher,
# which is annoying if you have to do it continuously; it would be
# better to use a persistent cache (although local, possibly
# filesystem-based instead of database-based) and somehow detect
# when the task has already been loaded
if task is None:
loader = YamlLoader(
os.path.realpath(os.path.join(base_dir, "..")),
file_cacher)
# Normally we should import the contest before, but YamlLoader
# accepts get_task() even without previous get_contest() calls
task = loader.get_task(os.path.split(os.path.realpath(base_dir))[1])
# Prepare the EvaluationJob
dataset = task.active_dataset
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, soluzione),
"Solution %s for task %s" % (soluzione, task.name))
executables = {task.name: Executable(filename=task.name, digest=digest)}
jobs = [(t, EvaluationJob(
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input, output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases]
tasktype = get_task_type(dataset=dataset)
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print jobinfo[0],
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
status = job.plus["exit_status"]
info.append("Time: %5.3f Wall: %5.3f Memory: %s" %
(job.plus["execution_time"],
job.plus["execution_wall_clock_time"],
mem_human(job.plus["execution_memory"])))
points.append(float(job.outcome))
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print
print "Want to stop and consider everything to timeout? [y/N]",
if assume is not None:
print assume
tmp = assume
else:
tmp = raw_input().lower()
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
# Result pretty printing
print
clen = max(len(c) for c in comments)
ilen = max(len(i) for i in info)
for (i, p, c, b) in zip(tcnames, points, comments, info):
print "%s) %5.2lf --- %s [%s]" % (i, p, c.ljust(clen), b.center(ilen))
return zip(points, comments, info)
def test_testcases(base_dir, soluzione, language, assume=None):
global task, file_cacher
# Use a disabled FileCacher with a FSBackend in order to avoid to fill
# the database with junk and to save up space.
if file_cacher is None:
file_cacher = FileCacher(path=os.path.join(config.cache_dir,
'cmsMake'),
enabled=False)
# Load the task
if task is None:
loader = YamlLoader(
os.path.realpath(os.path.join(base_dir, "..")),
file_cacher)
# Normally we should import the contest before, but YamlLoader
# accepts get_task() even without previous get_contest() calls
task = loader.get_task(os.path.split(os.path.realpath(base_dir))[1])
# Prepare the EvaluationJob
dataset = task.active_dataset
if dataset.task_type != "OutputOnly":
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, soluzione),
"Solution %s for task %s" % (soluzione, task.name))
executables = {task.name: Executable(filename=task.name,
digest=digest)}
jobs = [(t, EvaluationJob(
language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases]
tasktype = get_task_type(dataset=dataset)
else:
print("Generating outputs...", end='')
files = {}
for t in sorted(dataset.testcases.keys()):
with file_cacher.get_file(dataset.testcases[t].input) as fin:
with TemporaryFile() as fout:
print(str(t), end='')
call(soluzione, stdin=fin, stdout=fout, cwd=base_dir)
fout.seek(0)
digest = file_cacher.put_file_from_fobj(fout)
outname = "output_%s.txt" % t
files[outname] = File(filename=outname, digest=digest)
jobs = [(t, EvaluationJob(
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
files=files,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases]
for k, job in jobs:
job._key = k
tasktype = get_task_type(dataset=dataset)
print()
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0], end='')
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
if dataset.task_type != "OutputOnly":
status = job.plus["exit_status"]
info.append("Time: %5.3f Wall: %5.3f Memory: %s" %
(job.plus["execution_time"],
job.plus["execution_wall_clock_time"],
mem_human(job.plus["execution_memory"])))
else:
status = "ok"
info.append("N/A")
points.append(float(job.outcome))
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print()
print("Want to stop and consider everything to timeout? [y/N]",
end='')
if assume is not None:
print(assume)
tmp = assume
else:
tmp = raw_input().lower()
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
# Result pretty printing
print()
clen = max(len(c) for c in comments)
ilen = max(len(i) for i in info)
for (i, p, c, b) in zip(tcnames, points, comments, info):
print("%s) %5.2lf --- %s [%s]" % (i, p, c.ljust(clen), b.center(ilen)))
return zip(points, comments, info)