wehub-resource-sync
85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
from mlflow.entities._mlflow_object import _MlflowObject
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from mlflow.entities.metric import Metric
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from mlflow.entities.param import Param
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from mlflow.entities.run_tag import RunTag
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from mlflow.protos.service_pb2 import Param as ProtoParam
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from mlflow.protos.service_pb2 import RunData as ProtoRunData
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from mlflow.protos.service_pb2 import RunTag as ProtoRunTag
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class RunData(_MlflowObject):
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"""
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Run data (metrics and parameters).
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"""
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def __init__(self, metrics=None, params=None, tags=None):
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"""Construct a new mlflow.entities.RunData instance.
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Args:
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metrics: List of mlflow.entities.Metric.
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params: List of mlflow.entities.Param.
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tags: List of mlflow.entities.RunTag.
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"""
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# Maintain the original list of metrics so that we can easily convert it back to
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# protobuf
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self._metric_objs = metrics or []
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self._metrics = {metric.key: metric.value for metric in self._metric_objs}
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self._params = {param.key: param.value for param in (params or [])}
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self._tags = {tag.key: tag.value for tag in (tags or [])}
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@property
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def metrics(self):
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"""
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Dictionary of string key -> metric value for the current run.
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For each metric key, the metric value with the latest timestamp is returned. In case there
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are multiple values with the same latest timestamp, the maximum of these values is returned.
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"""
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return self._metrics
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@property
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def params(self):
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"""Dictionary of param key (string) -> param value for the current run."""
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return self._params
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@property
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def tags(self):
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"""Dictionary of tag key (string) -> tag value for the current run."""
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return self._tags
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def _add_metric(self, metric):
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self._metrics[metric.key] = metric.value
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self._metric_objs.append(metric)
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def _add_param(self, param):
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self._params[param.key] = param.value
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def _add_tag(self, tag):
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self._tags[tag.key] = tag.value
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def to_proto(self):
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run_data = ProtoRunData()
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run_data.metrics.extend([m.to_proto() for m in self._metric_objs])
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run_data.params.extend([ProtoParam(key=key, value=val) for key, val in self.params.items()])
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run_data.tags.extend([ProtoRunTag(key=key, value=val) for key, val in self.tags.items()])
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return run_data
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def to_dictionary(self):
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return {
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"metrics": self.metrics,
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"params": self.params,
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"tags": self.tags,
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}
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@classmethod
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def from_proto(cls, proto):
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run_data = cls()
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# iterate proto and add metrics, params, and tags
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for proto_metric in proto.metrics:
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run_data._add_metric(Metric.from_proto(proto_metric))
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for proto_param in proto.params:
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run_data._add_param(Param.from_proto(proto_param))
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for proto_tag in proto.tags:
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run_data._add_tag(RunTag.from_proto(proto_tag))
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return run_data
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