class ModelBase(object):
def __init__(self):
self.model_output = None
self.mode = None
self.role = None
self.data_output = None
self.model_param = None
self.transfer_variable = None
self.flowid = ''
self.task_version_id = ''
self.need_one_vs_rest = False
self.tracker = None
self.cv_fold = 0
self.validation_freqs = None
self.component_properties = ComponentProperties()
self._summary = dict()
self._align_cache = dict()
def _init_runtime_parameters(self, component_parameters):
param_extractor = ParamExtract()
param = param_extractor.parse_param_from_config(self.model_param, component_parameters)
param.check()
self.role = self.component_properties.parse_component_param(component_parameters, param).role
self._init_model(param)
return param
@property
def need_cv(self):
return self.component_properties.need_cv
@property
def need_run(self):
return self.component_properties.need_run
@need_run.setter
def need_run(self, value: bool):
self.component_properties.need_run = value
def _init_model(self, model):
pass
def load_model(self, model_dict):
pass
def _parse_need_run(self, model_dict, model_meta_name):
meta_obj = list(model_dict.get('model').values())[0].get(model_meta_name)
need_run = meta_obj.need_run
# self.need_run = need_run
self.component_properties.need_run = need_run
def run(self, component_parameters=None, args=None):
self._init_runtime_parameters(component_parameters)
self.component_properties.parse_dsl_args(args)
running_funcs = self.component_properties.extract_running_rules(args, self)
LOGGER.debug(f"running_funcs: {running_funcs.todo_func_list}")
saved_result = []
for func, params, save_result, use_previews in running_funcs:
# for func, params in zip(todo_func_list, todo_func_params):
if use_previews:
if params:
real_param = [saved_result, params]
else:
real_param = saved_result
LOGGER.debug("func: {}".format(func))
this_data_output = func(*real_param)
saved_result = []
else:
this_data_output = func(*params)
if save_result:
saved_result.append(this_data_output)
if len(saved_result) == 1:
self.data_output = saved_result[0]
# LOGGER.debug("One data: {}".format(self.data_output.first()[1].features))
LOGGER.debug("saved_result is : {}, data_output: {}".format(saved_result, self.data_output))
# self.check_consistency()
self.save_summary()
def get_metrics_param(self):
return EvaluateParam(eval_type="binary",
pos_label=1)
def check_consistency(self):
if not is_table(self.data_output):
return
if self.component_properties.input_data_count + self.component_properties.input_eval_data_count != \
self.data_output.count() and \
self.component_properties.input_data_count != self.component_properties.input_eval_data_count:
raise ValueError("Input data count does not match with output data count")
def predict(self, data_inst):
pass
def fit(self, *args):
pass
def transform(self, data_inst):
pass
def cross_validation(self, data_inst):
pass
def stepwise(self, data_inst):
pass
def one_vs_rest_fit(self, train_data=None):
pass
def one_vs_rest_predict(self, train_data):
pass
def init_validation_strategy(self, train_data=None, validate_data=None):
pass
def save_data(self):
return self.data_output
def export_model(self):
return self.model_output
def set_flowid(self, flowid):
# self.flowid = '.'.join([self.task_version_id, str(flowid)])
self.flowid = flowid
self.set_transfer_variable()
def set_transfer_variable(self):
if self.transfer_variable is not None:
LOGGER.debug("set flowid to transfer_variable, flowid: {}".format(self.flowid))
self.transfer_variable.set_flowid(self.flowid)
def set_task_version_id(self, task_version_id):
""" task_version_id: jobid + component_name, reserved variable """
self.task_version_id = task_version_id
def get_metric_name(self, name_prefix):
if not self.need_cv:
return name_prefix
return '_'.join(map(str, [name_prefix, self.flowid]))
def set_tracker(self, tracker):
self.tracker = tracker
@staticmethod
def set_predict_data_schema(predict_datas, schemas):
if predict_datas is None:
return predict_datas
if isinstance(predict_datas, list):
predict_data = predict_datas[0]
schema = schemas[0]
else:
predict_data = predict_datas
schema = schemas
if predict_data is not None:
predict_data.schema = {"header": ["label", "predict_result", "predict_score", "predict_detail", "type"],
"sid_name": schema.get('sid_name')}
return predict_data
@staticmethod
def predict_score_to_output(data_instances, predict_score, classes=None, threshold=0.5):
"""
Get predict result output
Parameters
----------
data_instances: table, data used for prediction
predict_score: table, probability scores
classes: list or None, all classes/label names
threshold: float, predict threshold, used for binary label
Returns
-------
Table, predict result
"""
# regression
if classes is None:
predict_result = data_instances.join(predict_score, lambda d, pred: [d.label, pred,
pred, {"label": pred}])
# binary
elif isinstance(classes, list) and len(classes) == 2:
class_neg, class_pos = classes[0], classes[1]
pred_label = predict_score.mapValues(lambda x: class_pos if x > threshold else class_neg)
predict_result = data_instances.mapValues(lambda x: x.label)
predict_result = predict_result.join(predict_score, lambda x, y: (x, y))
class_neg_name, class_pos_name = str(class_neg), str(class_pos)
predict_result = predict_result.join(pred_label, lambda x, y: [x[0], y, x[1],
{class_neg_name: (1 - x[1]),
class_pos_name: x[1]}])
# multi-label: input = array of predicted score of all labels
elif isinstance(classes, list) and len(classes) > 2:
# pred_label = predict_score.mapValues(lambda x: classes[x.index(max(x))])
classes = [str(val) for val in classes]
predict_result = data_instances.mapValues(lambda x: x.label)
predict_result = predict_result.join(predict_score, lambda x, y: [x, int(classes[np.argmax(y)]),
float(np.max(y)),
dict(zip(classes, list(y)))])
else:
raise ValueError(f"Model's classes type is {type(classes)}, classes must be None or list of length no less than 2.")
return predict_result
def callback_meta(self, metric_name, metric_namespace, metric_meta):
if self.need_cv:
metric_name = '.'.join([metric_name, str(self.cv_fold)])
flow_id_list = self.flowid.split('.')
LOGGER.debug("Need cv, change callback_meta, flow_id_list: {}".format(flow_id_list))
if len(flow_id_list) > 1:
curve_name = '.'.join(flow_id_list[1:])
metric_meta.update_metas({'curve_name': curve_name})
else:
metric_meta.update_metas({'curve_name': metric_name})
self.tracker.set_metric_meta(metric_name=metric_name,
metric_namespace=metric_namespace,
metric_meta=metric_meta)
def callback_metric(self, metric_name, metric_namespace, metric_data):
if self.need_cv:
metric_name = '.'.join([metric_name, str(self.cv_fold)])
self.tracker.log_metric_data(metric_name=metric_name,
metric_namespace=metric_namespace,
metrics=metric_data)
def save_summary(self):
self.tracker.log_component_summary(summary_data=self.summary())
def set_cv_fold(self, cv_fold):
self.cv_fold = cv_fold
def summary(self):
return copy.deepcopy(self._summary)
def set_summary(self, new_summary):
"""
Model summary setter
Parameters
----------
new_summary: dict, summary to replace the original one
Returns
-------
"""
if not isinstance(new_summary, dict):
raise ValueError(f"summary should be of dict type, received {type(new_summary)} instead.")
self._summary = copy.deepcopy(new_summary)
def add_summary(self, new_key, new_value):
"""
Add key:value pair to model summary
Parameters
----------
new_key: str
new_value: object
Returns
-------
"""
original_value = self._summary.get(new_key, None)
if original_value is not None:
LOGGER.warning(f"{new_key} already exists in model summary."
f"Corresponding value {original_value} will be replaced by {new_value}")
self._summary[new_key] = new_value
# LOGGER.debug(f"{new_key}: {new_value} added to summary.")
def merge_summary(self, new_content, suffix=None, suffix_sep='_'):
"""
Merge new content into model summary
Parameters
----------
new_content: dict, content to be merged into summary
suffix: str or None, suffix used to create new key if any key in new_content already exixts in model summary
suffix_sep: string, default '_', suffix separator used to create new key
Returns
-------
"""
if not isinstance(new_content, dict):
raise ValueError(f"To merge new content into model summary, "
f"value must be of dict type, received {type(new_content)} instead.")
new_summary = self.summary()
keyset = new_summary.keys() | new_content.keys()
for key in keyset:
if key in new_summary and key in new_content:
if suffix is not None:
new_key = f"{key}{suffix_sep}{suffix}"
else:
new_key = key
new_value = new_content.get(key)
new_summary[new_key] = new_value
elif key in new_content:
new_summary[key] = new_content.get(key)
else:
pass
self.set_summary(new_summary)
@staticmethod
def extract_data(data: dict):
LOGGER.debug("In extract_data, data input: {}".format(data))
if len(data) == 0:
return data
if len(data) == 1:
return list(data.values())[0]
return data
@staticmethod
def check_schema_content(schema):
"""
check for repeated header & illegal/non-printable chars except for space
allow non-ascii chars
:param schema: dict
:return:
"""
abnormal_detection.check_legal_schema(schema)
def align_data_header(self, data_instances, pre_header):
"""
align features of given data, raise error if value in given schema not found
:param data_instances: data table
:param pre_header: list, header of model
:return: dtable, aligned data
"""
result_data = self._align_cache.get(id(data_instances))
if result_data is None:
result_data = header_alignment(data_instances=data_instances, pre_header=pre_header)
self._align_cache[id(data_instances)] = result_data
return result_data
@staticmethod
def pass_data(data):
if isinstance(data, dict) and len(data) >= 1:
data = list(data.values())[0]
return data
def obtain_data(self, data_list):
if isinstance(data_list, list):
return data_list[0]
return data_list
class ModelBase(object):
def __init__(self):
self.model_output = None
self.mode = None
self.role = None
self.data_output = None
self.model_param = None
self.transfer_variable = None
self.flowid = ''
self.taskid = ''
self.need_one_vs_rest = False
self.tracker = None
self.cv_fold = 0
self.validation_freqs = None
self.component_properties = ComponentProperties()
def _init_runtime_parameters(self, component_parameters):
param_extracter = ParamExtract()
param = param_extracter.parse_param_from_config(
self.model_param, component_parameters)
param.check()
self.role = self.component_properties.parse_component_param(
component_parameters, param).role
self._init_model(param)
return param
@property
def need_cv(self):
return self.component_properties.need_cv
@property
def need_run(self):
return self.component_properties.need_run
@need_run.setter
def need_run(self, value: bool):
self.component_properties.need_run = value
def _init_model(self, model):
pass
def load_model(self, model_dict):
pass
def _parse_need_run(self, model_dict, model_meta_name):
meta_obj = list(
model_dict.get('model').values())[0].get(model_meta_name)
need_run = meta_obj.need_run
# self.need_run = need_run
self.component_properties.need_run = need_run
def run(self, component_parameters=None, args=None):
self._init_runtime_parameters(component_parameters)
self.component_properties.parse_dsl_args(args)
running_funcs = self.component_properties.extract_running_rules(
args, self)
saved_result = []
for func, params, save_result, use_previews in running_funcs:
# for func, params in zip(todo_func_list, todo_func_params):
if use_previews:
if params:
real_param = [saved_result, params]
else:
real_param = saved_result
LOGGER.debug("func: {}".format(func))
this_data_output = func(*real_param)
saved_result = []
else:
this_data_output = func(*params)
if save_result:
saved_result.append(this_data_output)
if len(saved_result) == 1:
self.data_output = saved_result[0]
# LOGGER.debug("One data: {}".format(self.data_output.first()[1].features))
LOGGER.debug("saved_result is : {}, data_output: {}".format(
saved_result, self.data_output))
self.check_consistency()
def get_metrics_param(self):
return EvaluateParam(eval_type="binary", pos_label=1)
def check_consistency(self):
if not type(self.data_output) in ["DTable", "RDDTable"]:
return
if self.component_properties.input_data_count + self.component_properties.input_eval_data_count != \
self.data_output.count():
raise ValueError(
"Input data count does not match with output data count")
def predict(self, data_inst):
pass
def fit(self, *args):
pass
def transform(self, data_inst):
pass
def cross_validation(self, data_inst):
pass
def stepwise(self, data_inst):
pass
def one_vs_rest_fit(self, train_data=None):
pass
def one_vs_rest_predict(self, train_data):
pass
def init_validation_strategy(self, train_data=None, validate_data=None):
pass
def save_data(self):
return self.data_output
def export_model(self):
return self.model_output
def set_flowid(self, flowid):
# self.flowid = '.'.join([self.taskid, str(flowid)])
self.flowid = flowid
self.set_transfer_variable()
def set_transfer_variable(self):
if self.transfer_variable is not None:
LOGGER.debug("set flowid to transfer_variable, flowid: {}".format(
self.flowid))
self.transfer_variable.set_flowid(self.flowid)
def set_taskid(self, taskid):
""" taskid: jobid + component_name, reserved variable """
self.taskid = taskid
def get_metric_name(self, name_prefix):
if not self.need_cv:
return name_prefix
return '_'.join(map(str, [name_prefix, self.flowid]))
def set_tracker(self, tracker):
self.tracker = tracker
def set_predict_data_schema(self, predict_datas, schemas):
if predict_datas is None:
return predict_datas
if isinstance(predict_datas, list):
predict_data = predict_datas[0]
schema = schemas[0]
else:
predict_data = predict_datas
schema = schemas
if predict_data is not None:
predict_data.schema = {
"header": [
"label", "predict_result", "predict_score",
"predict_detail", "type"
],
"sid_name":
schema.get('sid_name')
}
return predict_data
def callback_meta(self, metric_name, metric_namespace, metric_meta):
if self.need_cv:
metric_name = '.'.join([metric_name, str(self.cv_fold)])
flow_id_list = self.flowid.split('.')
LOGGER.debug(
"Need cv, change callback_meta, flow_id_list: {}".format(
flow_id_list))
if len(flow_id_list) > 1:
curve_name = '.'.join(flow_id_list[1:])
metric_meta.update_metas({'curve_name': curve_name})
else:
metric_meta.update_metas({'curve_name': metric_name})
self.tracker.set_metric_meta(metric_name=metric_name,
metric_namespace=metric_namespace,
metric_meta=metric_meta)
def callback_metric(self, metric_name, metric_namespace, metric_data):
if self.need_cv:
metric_name = '.'.join([metric_name, str(self.cv_fold)])
self.tracker.log_metric_data(metric_name=metric_name,
metric_namespace=metric_namespace,
metrics=metric_data)
def set_cv_fold(self, cv_fold):
self.cv_fold = cv_fold
@staticmethod
def extract_data(data: dict):
LOGGER.debug("In extract_data, data input: {}".format(data))
if len(data) == 0:
return data
if len(data) == 1:
return list(data.values())[0]
return data
class ModelBase(object):
component_name = None
@classmethod
def set_component_name(cls, name):
cls.component_name = name
@classmethod
def get_component_name(cls):
return cls.component_name
def __init__(self):
self.model_output = None
self.mode = None
self.role = None
self.data_output = None
self.cache_output = None
self.model_param = None
self.transfer_variable = None
self.flowid = ""
self.task_version_id = ""
self.need_one_vs_rest = False
self.callback_one_vs_rest = False
self.checkpoint_manager = None
self.cv_fold = 0
self.validation_freqs = None
self.component_properties = ComponentProperties()
self._summary = dict()
self._align_cache = dict()
self._tracker = None
self.step_name = "step_name"
self.callback_list: CallbackList
self.callback_variables = CallbacksVariable()
@property
def tracker(self) -> WarpedTrackerClient:
if self._tracker is None:
raise RuntimeError(f"use tracker before set")
return self._tracker
@tracker.setter
def tracker(self, value):
self._tracker = WarpedTrackerClient(value)
@property
def stop_training(self):
return self.callback_variables.stop_training
@property
def need_cv(self):
return self.component_properties.need_cv
@property
def need_run(self):
return self.component_properties.need_run
@need_run.setter
def need_run(self, value: bool):
self.component_properties.need_run = value
def _init_model(self, model):
pass
def load_model(self, model_dict):
pass
def _parse_need_run(self, model_dict, model_meta_name):
meta_obj = list(model_dict.get("model").values())[0].get(model_meta_name)
need_run = meta_obj.need_run
# self.need_run = need_run
self.component_properties.need_run = need_run
def run(self, cpn_input, retry: bool = True):
self.task_version_id = cpn_input.task_version_id
self.tracker = cpn_input.tracker
self.checkpoint_manager = cpn_input.checkpoint_manager
deserialize_models(cpn_input.models)
# retry
if (
self._retry
and self.checkpoint_manager is not None
and self.checkpoint_manager.latest_checkpoint is not None
):
self._retry(cpn_input=cpn_input)
# normal
else:
self._run(cpn_input=cpn_input)
return ComponentOutput(self.save_data(), self._export(), self.save_cache())
def _export(self):
# export model
try:
model = self._export_model()
meta = self._export_meta()
export_dict = {"Meta": meta, "Param": model}
except NotImplementedError:
export_dict = self.export_model()
# export nothing, return
if export_dict is None:
return export_dict
try:
meta_name = [k for k in export_dict if k.endswith("Meta")][0]
except BaseException:
raise KeyError("Meta not found in export model")
try:
param_name = [k for k in export_dict if k.endswith("Param")][0]
except BaseException:
raise KeyError("Param not found in export model")
meta = export_dict[meta_name]
# set component name
if hasattr(meta, "component"):
meta.component = self.get_component_name()
else:
import warnings
warnings.warn(f"{meta} should add `component` field")
return export_dict
def _export_meta(self):
raise NotImplementedError("_export_meta not implemented")
def _export_model(self):
raise NotImplementedError("_export_model not implemented")
def _run(self, cpn_input) -> None:
# paramters
self.model_param.update(cpn_input.parameters)
self.model_param.check()
self.component_properties.parse_component_param(
cpn_input.roles, self.model_param
)
self.role = self.component_properties.role
self.component_properties.parse_dsl_args(cpn_input.datasets, cpn_input.models)
self.component_properties.parse_caches(cpn_input.caches)
# init component, implemented by subclasses
self._init_model(self.model_param)
self.callback_list = CallbackList(self.role, self.mode, self)
if hasattr(self.model_param, "callback_param"):
callback_param = getattr(self.model_param, "callback_param")
self.callback_list.init_callback_list(callback_param)
running_funcs = self.component_properties.extract_running_rules(
datasets=cpn_input.datasets, models=cpn_input.models, cpn=self
)
LOGGER.debug(f"running_funcs: {running_funcs.todo_func_list}")
saved_result = []
for func, params, save_result, use_previews in running_funcs:
# for func, params in zip(todo_func_list, todo_func_params):
if use_previews:
if params:
real_param = [saved_result, params]
else:
real_param = saved_result
LOGGER.debug("func: {}".format(func))
this_data_output = func(*real_param)
saved_result = []
else:
this_data_output = func(*params)
if save_result:
saved_result.append(this_data_output)
if len(saved_result) == 1:
self.data_output = saved_result[0]
# LOGGER.debug("One data: {}".format(self.data_output.first()[1].features))
LOGGER.debug(
"saved_result is : {}, data_output: {}".format(
saved_result, self.data_output
)
)
# self.check_consistency()
self.save_summary()
def _retry(self, cpn_input) -> None:
self.model_param.update(cpn_input.parameters)
self.model_param.check()
self.component_properties.parse_component_param(
cpn_input.roles, self.model_param
)
self.role = self.component_properties.role
self.component_properties.parse_dsl_args(cpn_input.datasets, cpn_input.models)
self.component_properties.parse_caches(cpn_input.caches)
# init component, implemented by subclasses
self._init_model(self.model_param)
self.callback_list = CallbackList(self.role, self.mode, self)
if hasattr(self.model_param, "callback_param"):
callback_param = getattr(self.model_param, "callback_param")
self.callback_list.init_callback_list(callback_param)
(
train_data,
validate_data,
test_data,
data,
) = self.component_properties.extract_input_data(
datasets=cpn_input.datasets, model=self
)
running_funcs = RunningFuncs()
latest_checkpoint = self.get_latest_checkpoint()
running_funcs.add_func(self.load_model, [latest_checkpoint])
running_funcs = self.component_properties.warm_start_process(
running_funcs, self, train_data, validate_data
)
LOGGER.debug(f"running_funcs: {running_funcs.todo_func_list}")
self._execute_running_funcs(running_funcs)
def _execute_running_funcs(self, running_funcs):
saved_result = []
for func, params, save_result, use_previews in running_funcs:
# for func, params in zip(todo_func_list, todo_func_params):
if use_previews:
if params:
real_param = [saved_result, params]
else:
real_param = saved_result
LOGGER.debug("func: {}".format(func))
detected_func = assert_match_id_consistent(func)
this_data_output = detected_func(*real_param)
saved_result = []
else:
detected_func = assert_match_id_consistent(func)
this_data_output = detected_func(*params)
if save_result:
saved_result.append(this_data_output)
if len(saved_result) == 1:
self.data_output = saved_result[0]
LOGGER.debug(
"saved_result is : {}, data_output: {}".format(
saved_result, self.data_output
)
)
self.save_summary()
def export_serialized_models(self):
return serialize_models(self.export_model())
def get_metrics_param(self):
return EvaluateParam(eval_type="binary", pos_label=1)
def check_consistency(self):
if not is_table(self.data_output):
return
if (
self.component_properties.input_data_count
+ self.component_properties.input_eval_data_count
!= self.data_output.count()
and self.component_properties.input_data_count
!= self.component_properties.input_eval_data_count
):
raise ValueError("Input data count does not match with output data count")
def predict(self, data_inst):
pass
def fit(self, *args):
pass
def transform(self, data_inst):
pass
def cross_validation(self, data_inst):
pass
def stepwise(self, data_inst):
pass
def one_vs_rest_fit(self, train_data=None):
pass
def one_vs_rest_predict(self, train_data):
pass
def init_validation_strategy(self, train_data=None, validate_data=None):
pass
def save_data(self):
return self.data_output
def export_model(self):
return self.model_output
def save_cache(self):
return self.cache_output
def set_flowid(self, flowid):
# self.flowid = '.'.join([self.task_version_id, str(flowid)])
self.flowid = flowid
self.set_transfer_variable()
def set_transfer_variable(self):
if self.transfer_variable is not None:
LOGGER.debug(
"set flowid to transfer_variable, flowid: {}".format(self.flowid)
)
self.transfer_variable.set_flowid(self.flowid)
def set_task_version_id(self, task_version_id):
"""task_version_id: jobid + component_name, reserved variable"""
self.task_version_id = task_version_id
def get_metric_name(self, name_prefix):
if not self.need_cv:
return name_prefix
return "_".join(map(str, [name_prefix, self.flowid]))
def set_tracker(self, tracker):
self._tracker = tracker
def set_checkpoint_manager(self, checkpoint_manager):
checkpoint_manager.load_checkpoints_from_disk()
self.checkpoint_manager = checkpoint_manager
@staticmethod
def set_predict_data_schema(predict_datas, schemas):
if predict_datas is None:
return predict_datas
if isinstance(predict_datas, list):
predict_data = predict_datas[0]
schema = schemas[0]
else:
predict_data = predict_datas
schema = schemas
if predict_data is not None:
predict_data.schema = {
"header": [
"label",
"predict_result",
"predict_score",
"predict_detail",
"type",
],
"sid_name": schema.get("sid_name"),
"content_type": "predict_result",
}
return predict_data
@staticmethod
def predict_score_to_output(
data_instances, predict_score, classes=None, threshold=0.5
):
"""
Get predict result output
Parameters
----------
data_instances: table, data used for prediction
predict_score: table, probability scores
classes: list or None, all classes/label names
threshold: float, predict threshold, used for binary label
Returns
-------
Table, predict result
"""
# regression
if classes is None:
predict_result = data_instances.join(
predict_score, lambda d, pred: [d.label, pred, pred, {"label": pred}]
)
# binary
elif isinstance(classes, list) and len(classes) == 2:
class_neg, class_pos = classes[0], classes[1]
pred_label = predict_score.mapValues(
lambda x: class_pos if x > threshold else class_neg
)
predict_result = data_instances.mapValues(lambda x: x.label)
predict_result = predict_result.join(predict_score, lambda x, y: (x, y))
class_neg_name, class_pos_name = str(class_neg), str(class_pos)
predict_result = predict_result.join(
pred_label,
lambda x, y: [
x[0],
y,
x[1],
{class_neg_name: (1 - x[1]), class_pos_name: x[1]},
],
)
# multi-label: input = array of predicted score of all labels
elif isinstance(classes, list) and len(classes) > 2:
# pred_label = predict_score.mapValues(lambda x: classes[x.index(max(x))])
classes = [str(val) for val in classes]
predict_result = data_instances.mapValues(lambda x: x.label)
predict_result = predict_result.join(
predict_score,
lambda x, y: [
x,
int(classes[np.argmax(y)]),
float(np.max(y)),
dict(zip(classes, list(y))),
],
)
else:
raise ValueError(
f"Model's classes type is {type(classes)}, classes must be None or list of length no less than 2."
)
def _transfer(instance, pred_res):
return Instance(features=pred_res, inst_id=instance.inst_id)
predict_result = data_instances.join(predict_result, _transfer)
return predict_result
def callback_meta(self, metric_name, metric_namespace, metric_meta: MetricMeta):
if self.need_cv:
metric_name = ".".join([metric_name, str(self.cv_fold)])
flow_id_list = self.flowid.split(".")
LOGGER.debug(
"Need cv, change callback_meta, flow_id_list: {}".format(flow_id_list)
)
if len(flow_id_list) > 1:
curve_name = ".".join(flow_id_list[1:])
metric_meta.update_metas({"curve_name": curve_name})
else:
metric_meta.update_metas({"curve_name": metric_name})
self.tracker.set_metric_meta(
metric_name=metric_name,
metric_namespace=metric_namespace,
metric_meta=metric_meta,
)
def callback_metric(
self, metric_name, metric_namespace, metric_data: typing.List[Metric]
):
if self.need_cv:
metric_name = ".".join([metric_name, str(self.cv_fold)])
self.tracker.log_metric_data(
metric_name=metric_name,
metric_namespace=metric_namespace,
metrics=metric_data,
)
def callback_warm_start_init_iter(self, iter_num):
metric_meta = MetricMeta(
name="train",
metric_type="init_iter",
extra_metas={
"unit_name": "iters",
},
)
self.callback_meta(
metric_name="init_iter", metric_namespace="train", metric_meta=metric_meta
)
self.callback_metric(
metric_name="init_iter",
metric_namespace="train",
metric_data=[Metric("init_iter", iter_num)],
)
def get_latest_checkpoint(self):
return self.checkpoint_manager.latest_checkpoint.read()
def save_summary(self):
self.tracker.log_component_summary(summary_data=self.summary())
def set_cv_fold(self, cv_fold):
self.cv_fold = cv_fold
def summary(self):
return copy.deepcopy(self._summary)
def set_summary(self, new_summary):
"""
Model summary setter
Parameters
----------
new_summary: dict, summary to replace the original one
Returns
-------
"""
if not isinstance(new_summary, dict):
raise ValueError(
f"summary should be of dict type, received {type(new_summary)} instead."
)
self._summary = copy.deepcopy(new_summary)
def add_summary(self, new_key, new_value):
"""
Add key:value pair to model summary
Parameters
----------
new_key: str
new_value: object
Returns
-------
"""
original_value = self._summary.get(new_key, None)
if original_value is not None:
LOGGER.warning(
f"{new_key} already exists in model summary."
f"Corresponding value {original_value} will be replaced by {new_value}"
)
self._summary[new_key] = new_value
# LOGGER.debug(f"{new_key}: {new_value} added to summary.")
def merge_summary(self, new_content, suffix=None, suffix_sep="_"):
"""
Merge new content into model summary
Parameters
----------
new_content: dict, content to be merged into summary
suffix: str or None, suffix used to create new key if any key in new_content already exixts in model summary
suffix_sep: string, default '_', suffix separator used to create new key
Returns
-------
"""
if not isinstance(new_content, dict):
raise ValueError(
f"To merge new content into model summary, "
f"value must be of dict type, received {type(new_content)} instead."
)
new_summary = self.summary()
keyset = new_summary.keys() | new_content.keys()
for key in keyset:
if key in new_summary and key in new_content:
if suffix is not None:
new_key = f"{key}{suffix_sep}{suffix}"
else:
new_key = key
new_value = new_content.get(key)
new_summary[new_key] = new_value
elif key in new_content:
new_summary[key] = new_content.get(key)
else:
pass
self.set_summary(new_summary)
@staticmethod
def extract_data(data: dict):
LOGGER.debug("In extract_data, data input: {}".format(data))
if len(data) == 0:
return data
if len(data) == 1:
return list(data.values())[0]
return data
@staticmethod
def check_schema_content(schema):
"""
check for repeated header & illegal/non-printable chars except for space
allow non-ascii chars
:param schema: dict
:return:
"""
abnormal_detection.check_legal_schema(schema)
def align_data_header(self, data_instances, pre_header):
"""
align features of given data, raise error if value in given schema not found
:param data_instances: data table
:param pre_header: list, header of model
:return: dtable, aligned data
"""
result_data = self._align_cache.get(id(data_instances))
if result_data is None:
result_data = header_alignment(
data_instances=data_instances, pre_header=pre_header
)
self._align_cache[id(data_instances)] = result_data
return result_data
@staticmethod
def pass_data(data):
if isinstance(data, dict) and len(data) >= 1:
data = list(data.values())[0]
return data
def obtain_data(self, data_list):
if isinstance(data_list, list):
return data_list[0]
return data_list