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scikit_learn.py
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scikit_learn.py
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from typing import Any, Callable, Dict, Mapping, Union
import sklearn
import torch
from catalyst.callbacks.metric import FunctionalBatchMetricCallback
from catalyst.core.runner import IRunner
from catalyst.metrics import FunctionalBatchMetric
class SklearnCallback(FunctionalBatchMetricCallback):
"""
Args:
keys:
metric_fn:
metric_key:
log_on_batch:
"""
def __init__(
self,
keys: Mapping[str, Any],
metric_fn: Union[Callable, str],
metric_key: str,
log_on_batch: bool = True,
):
"""Init."""
if isinstance(metric_fn, str):
metric_fn = sklearn.metrics.__dict__[metric_fn]
super().__init__(
metric=FunctionalBatchMetric(metric_fn=metric_fn, metric_key=metric_key),
input_key=keys,
target_key=keys,
log_on_batch=log_on_batch,
)
def _get_key_value_inputs(self, runner: "IRunner") -> Dict[str, torch.Tensor]:
"""@TODO: Docs."""
kv_inputs = {}
for key, value in self._keys.items():
if value in runner.batch:
kv_inputs[key] = runner.batch[value].cpu().detach().numpy()
else:
kv_inputs[key] = self._keys[key]
kv_inputs["batch_size"] = runner.batch_size
return kv_inputs