class ContinuousFeatureExtension(Bean):
bins = ListBeanField(ContinuousFeatureBin)
min = FloatField()
max = FloatField()
mean = FloatField()
stddev = FloatField()
median = FloatField()
value_count = ListBeanField(FeatureValueCount)
class JobStep(Bean):
type = StringField()
status = StringField()
took = FloatField()
datetime = IntegerField()
extension = DictField()
class Status:
Succeed = "succeed"
Failed = "failed"
class RegressionTaskMetrics(Bean):
mse = FloatField()
mae = FloatField()
msle = FloatField()
rmse = FloatField()
rootmeansquarederror = FloatField()
r2 = FloatField()
class FeatureMissing(Bean):
value = IntegerField()
percentage = FloatField()
status = StringField()
class Status:
TooHigh = 'too_high'
@staticmethod
def calc_status(percentage):
if percentage > 70:
return FeatureMissing.Status.TooHigh
else:
return FeatureNormalStatus
class FeatureUnique(Bean):
value = IntegerField()
percentage = FloatField()
status = StringField()
class Status:
ID_ness = 'ID-ness'
Stable = 'stable'
@staticmethod
def calc_status(n_uniques, percentage):
if n_uniques == 1:
return FeatureUnique.Status.Stable
else:
if percentage > 90:
return FeatureUnique.Status.ID_ness
else:
return FeatureNormalStatus
class FeatureCorrelation(Bean):
value = FloatField()
status = StringField()
class Status:
TooHigh = 'too_high'
TooLow = 'too_low'
@staticmethod
def calc_status(correlation, is_target_col):
_c = abs(correlation)
if _c > 0.5:
if is_target_col is True:
return FeatureNormalStatus
else:
return FeatureCorrelation.Status.TooHigh
elif _c < 0.01:
return FeatureCorrelation.Status.TooLow
else:
return FeatureNormalStatus
class FeatureMode(Bean):
value = StringField()
count = IntegerField()
percentage = FloatField()
class FeatureValueCount(Bean):
type = ObjectField()
value = IntegerField()
percentage = FloatField()
class Model(Bean):
name = StringField()
framework = StringField()
dataset_name = StringField()
model_file_size = IntegerField()
no_experiment = IntegerField()
inputs = ListBeanField(ModelFeature)
task_type = StringField()
performance = BeanField(Performance)
model_path = StringField()
status = StringField()
pid = IntegerField()
score = FloatField()
progress = StringField()
train_job_name = StringField()
train_trail_no = IntegerField()
trails = ListBeanField(TrainTrail)
extension = DictField()
create_datetime = DatetimeField()
finish_datetime = DatetimeField()
last_update_datetime = DatetimeField()
def escaped_time(self):
if self.status in [ModelStatusType.Succeed, ModelStatusType.Failed]:
if self.finish_datetime is None:
raise Exception(
"Internal error, train finished but has no finish_datetime. "
)
escaped = util.datetime_diff_human_format_by_minute(
self.finish_datetime, self.create_datetime)
else:
escaped = util.datetime_diff_human_format_by_minute(
util.get_now_datetime(), self.create_datetime)
return escaped
def escaped_time_by_seconds(self):
if self.status in [ModelStatusType.Succeed, ModelStatusType.Failed]:
if self.finish_datetime is None:
raise Exception(
f"Internal error, model name = {self.name} train finished but has no finish_datetime. "
)
escaped = util.datetime_diff(self.finish_datetime,
self.create_datetime)
else:
escaped = util.datetime_diff(util.get_now_datetime(),
self.create_datetime)
return escaped
def default_metric(self):
m = \
{
'multi_classification': "logloss",
'regression': "mae",
'binary_classification': "auc"
}
return m[self.task_type]
def log_file_path(self):
# exits begin from train start
return util.relative_path(P.join(str(self.model_path), 'train.log'))
def train_source_code_path(self):
# exits begin from train start
return util.relative_path(P.join(str(self.model_path), 'train.py'))
def train_notebook_uri(self):
# exits begin from train start
train_notebook_path = P.join(str(self.model_path), 'train.ipynb')
return util.relative_path(train_notebook_path)
class TrainTrail(Bean):
trail_no = IntegerField()
reward = FloatField()
elapsed = FloatField()
params = DictField()
class ClassifyTaskMetrics(Bean):
auc = FloatField()
accuracy = FloatField()
recall = FloatField()
precision = FloatField()
f1 = FloatField()