"""Class for concept-based XAI methods."""
from abc import ABC, abstractmethod
from typing import Any
import torch
from sklearn import metrics
from datasets import Features, Value, Sequence, ClassLabel
from lczerolens.board import LczeroBoard
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class Concept(ABC):
"""
Class for concept-based XAI methods.
"""
@abstractmethod
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def compute_label(
self,
board: LczeroBoard,
) -> Any:
"""
Compute the label for a given model and input.
"""
pass
@staticmethod
@abstractmethod
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def compute_metrics(
predictions,
labels,
):
"""
Compute the metrics for a given model and input.
"""
pass
@property
@abstractmethod
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def features(self) -> Features:
"""
Return the features for the concept.
"""
pass
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class BinaryConcept(Concept):
"""
Class for binary concept-based XAI methods.
"""
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features = Features(
{
"gameid": Value("string"),
"moves": Sequence(Value("string")),
"fen": Value("string"),
"label": ClassLabel(num_classes=2),
}
)
@staticmethod
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def compute_metrics(
predictions,
labels,
):
"""
Compute the metrics for a given model and input.
"""
return {
"accuracy": metrics.accuracy_score(labels, predictions),
"precision": metrics.precision_score(labels, predictions),
"recall": metrics.recall_score(labels, predictions),
"f1": metrics.f1_score(labels, predictions),
}
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class NullConcept(BinaryConcept):
"""
Class for binary concept-based XAI methods.
"""
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def compute_label(
self,
board: LczeroBoard,
) -> Any:
"""
Compute the label for a given model and input.
"""
return 0
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class OrBinaryConcept(BinaryConcept):
"""
Class for binary concept-based XAI methods.
"""
def __init__(self, *concepts: BinaryConcept):
for concept in concepts:
if not isinstance(concept, BinaryConcept):
raise ValueError(f"{concept} is not a BinaryConcept")
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self.concepts = concepts
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def compute_label(
self,
board: LczeroBoard,
) -> Any:
"""
Compute the label for a given model and input.
"""
return any(concept.compute_label(board) for concept in self.concepts)
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class AndBinaryConcept(BinaryConcept):
"""
Class for binary concept-based XAI methods.
"""
def __init__(self, *concepts: BinaryConcept):
for concept in concepts:
if not isinstance(concept, BinaryConcept):
raise ValueError(f"{concept} is not a BinaryConcept")
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self.concepts = concepts
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def compute_label(
self,
board: LczeroBoard,
) -> Any:
"""
Compute the label for a given model and input.
"""
return all(concept.compute_label(board) for concept in self.concepts)
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class MulticlassConcept(Concept):
"""
Class for multiclass concept-based XAI methods.
"""
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features = Features(
{
"gameid": Value("string"),
"moves": Sequence(Value("string")),
"fen": Value("string"),
"label": Value("int32"),
}
)
@staticmethod
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def compute_metrics(
predictions,
labels,
):
"""
Compute the metrics for a given model and input.
"""
return {
"accuracy": metrics.accuracy_score(labels, predictions),
"precision": metrics.precision_score(labels, predictions, average="weighted"),
"recall": metrics.recall_score(labels, predictions, average="weighted"),
"f1": metrics.f1_score(labels, predictions, average="weighted"),
}
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class ContinuousConcept(Concept):
"""
Class for continuous concept-based XAI methods.
"""
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features = Features(
{
"gameid": Value("string"),
"moves": Sequence(Value("string")),
"fen": Value("string"),
"label": Value("float32"),
}
)
@staticmethod
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def compute_metrics(
predictions,
labels,
):
"""
Compute the metrics for a given model and input.
"""
return {
"rmse": metrics.root_mean_squared_error(labels, predictions),
"mae": metrics.mean_absolute_error(labels, predictions),
"r2": metrics.r2_score(labels, predictions),
}
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def concept_collate_fn(batch):
boards = []
labels = []
for element in batch:
board = LczeroBoard(element["fen"])
for move in element["moves"]:
board.push_san(move)
boards.append(board)
labels.append(element["label"])
return boards, labels, batch
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def concept_init_rel(output, infos):
labels = infos[0]
rel = torch.zeros_like(output)
for i in range(rel.shape[0]):
rel[i, labels[i]] = output[i, labels[i]]
return rel
