flwr.server.history 源代码

# Copyright 2020 Flower Labs GmbH. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""Training history."""


import pprint
from functools import reduce
from typing import Dict, List, Tuple

from flwr.common.typing import Scalar


[文档]class History: """History class for training and/or evaluation metrics collection.""" def __init__(self) -> None: self.losses_distributed: List[Tuple[int, float]] = [] self.losses_centralized: List[Tuple[int, float]] = [] self.metrics_distributed_fit: Dict[str, List[Tuple[int, Scalar]]] = {} self.metrics_distributed: Dict[str, List[Tuple[int, Scalar]]] = {} self.metrics_centralized: Dict[str, List[Tuple[int, Scalar]]] = {}
[文档] def add_loss_distributed(self, server_round: int, loss: float) -> None: """Add one loss entry (from distributed evaluation).""" self.losses_distributed.append((server_round, loss))
[文档] def add_loss_centralized(self, server_round: int, loss: float) -> None: """Add one loss entry (from centralized evaluation).""" self.losses_centralized.append((server_round, loss))
[文档] def add_metrics_distributed_fit( self, server_round: int, metrics: Dict[str, Scalar] ) -> None: """Add metrics entries (from distributed fit).""" for key in metrics: # if not (isinstance(metrics[key], float) or isinstance(metrics[key], int)): # continue # ignore non-numeric key/value pairs if key not in self.metrics_distributed_fit: self.metrics_distributed_fit[key] = [] self.metrics_distributed_fit[key].append((server_round, metrics[key]))
[文档] def add_metrics_distributed( self, server_round: int, metrics: Dict[str, Scalar] ) -> None: """Add metrics entries (from distributed evaluation).""" for key in metrics: # if not (isinstance(metrics[key], float) or isinstance(metrics[key], int)): # continue # ignore non-numeric key/value pairs if key not in self.metrics_distributed: self.metrics_distributed[key] = [] self.metrics_distributed[key].append((server_round, metrics[key]))
[文档] def add_metrics_centralized( self, server_round: int, metrics: Dict[str, Scalar] ) -> None: """Add metrics entries (from centralized evaluation).""" for key in metrics: # if not (isinstance(metrics[key], float) or isinstance(metrics[key], int)): # continue # ignore non-numeric key/value pairs if key not in self.metrics_centralized: self.metrics_centralized[key] = [] self.metrics_centralized[key].append((server_round, metrics[key]))
def __repr__(self) -> str: """Create a representation of History. The representation consists of the following data (for each round) if present: * distributed loss. * centralized loss. * distributed training metrics. * distributed evaluation metrics. * centralized metrics. Returns ------- representation : str The string representation of the history object. """ rep = "" if self.losses_distributed: rep += "History (loss, distributed):\n" + pprint.pformat( reduce( lambda a, b: a + b, [ f"\tround {server_round}: {loss}\n" for server_round, loss in self.losses_distributed ], ) ) if self.losses_centralized: rep += "History (loss, centralized):\n" + pprint.pformat( reduce( lambda a, b: a + b, [ f"\tround {server_round}: {loss}\n" for server_round, loss in self.losses_centralized ], ) ) if self.metrics_distributed_fit: rep += "History (metrics, distributed, fit):\n" + pprint.pformat( self.metrics_distributed_fit ) if self.metrics_distributed: rep += "History (metrics, distributed, evaluate):\n" + pprint.pformat( self.metrics_distributed ) if self.metrics_centralized: rep += "History (metrics, centralized):\n" + pprint.pformat( self.metrics_centralized ) return rep