理解您的需求，我们将对项目1 (wine_QNNimprove.py 及其相关文件) 进行全面修改，使其实现无偏的重要性采样，同时保持量子优化器 (GroverOptimizer) 不变。具体而言，我们将：

避免每轮计算整个数据集的得分：仅选取一部分样本（例如B个）计算得分，类似项目2的做法。
确保无偏采样：通过适当计算和应用样本权重，使得采样过程保持无偏。
保持量子优化器不变：量子优化器将在选定的子集上继续工作。
以下是详细的修改步骤及相应的代码：

1. 修改 samplers.py 文件
首先，我们需要修改采样器，使其仅对选定的一部分样本（B个）计算得分，然后使用量子优化器从这部分样本中选择K个进行训练，同时计算并应用无偏权重。

修改后的 samplers.py

# samplers.py

import numpy as np
from blinker import signal

class BaseSampler(object):
    def __init__(self, dataset, reweighting):
        self.dataset = dataset
        self.reweighting = reweighting

    def _slice_data(self, x, y, idxs):
        if isinstance(x, (list, tuple)):
            return [xi[idxs] for xi in x], y[idxs]
        else:
            return x[idxs], y[idxs]

    def _send_messages(self, idxs, xy, w, predicted_scores):
        signal("is.sample").send({
            "idxs": idxs,
            "xy": xy,
            "w": w,
            "predicted_scores": predicted_scores
        })

    def _get_samples_with_scores(self, batch_size):
        raise NotImplementedError()

    def sample(self, batch_size):
        raise NotImplementedError()

    def update(self, idxs, results):
        pass

class VarianceReductionCondition(object):
    """Condition to check variance reduction."""
    def __init__(self, vr_th=1.2, momentum=0.9):
        self._vr_th = vr_th
        self._vr = 0.0
        self._previous_vr = 0.0
        self._momentum = momentum

    @property
    def satisfied(self):
        self._previous_vr = self._vr
        return self._vr > self._vr_th

    @property
    def previously_satisfied(self):
        return self._previous_vr > self._vr_th

    def update(self, scores):
        u = 1.0 / len(scores)
        S = scores.sum()
        if S == 0:
            g = np.array(u)
        else:
            g = scores / S
        new_vr = 1.0 / np.sqrt(1 - ((g - u) ** 2).sum() / (g ** 2).sum())
        self._vr = (
            self._momentum * self._vr +
            (1 - self._momentum) * new_vr
        )

class ConstantTimeSampler(BaseSampler):
    def __init__(self, dataset, reweighting, model, B=128, K=16,
                 backward_time=2.0, tau_th=1.2, ratio=0.5, min_a=0.2):
        """
        Args:
            dataset: The dataset to sample from.
            reweighting: Reweighting policy.
            model: The model wrapper with score method.
            B: Number of samples to score per batch.
            K: Number of samples to select via quantum optimizer.
            backward_time: Time multiplier for backward pass.
            tau_th: Variance reduction threshold.
            ratio: Ratio to determine additional samples.
            min_a: Minimum number of additional samples.
        """
        self.condition = VarianceReductionCondition(tau_th)
        self.backward_time = backward_time
        self.ratio = ratio
        self.min_a = min_a
        self.model = model
        self.B = B  # Subset size to score
        self.K = K  # Number of samples to select
        self.N = len(dataset.train_data) if hasattr(dataset, 'train_data') else _get_dataset_length(dataset, default=1)
        print(f"Dataset size (self.N): {self.N}")
        self.usage_counts = np.zeros(self.N, dtype=int)
        super(ConstantTimeSampler, self).__init__(dataset, reweighting)

    def _determine_devide(self, std_scores, batch_size):
        # Adjust threshold based on standard deviation
        if std_scores > 1.0:
            devide = int(batch_size * 0.7)
        elif std_scores > 0.5:
            devide = int(batch_size * 0.5)
        else:
            devide = int(batch_size * 0.3)
        return devide

    def _random_sample(self, batch_size):
        # Implement random sampling as a fallback strategy
        random_indices = np.random.choice(self.N, size=batch_size, replace=False)
        x, y = self.dataset.train_data[random_indices]
        w = np.ones((batch_size, 1))  # Assuming weight_size=1 for simplicity
        return random_indices, (x, y), w

    def sample(self, batch_size):
        print(f"Entering sample method")
        print(f"Requested batch_size: {batch_size}, self.N: {self.N}")
        print(f"Condition satisfied: {self.condition.satisfied}")

        # Step 1: Sample B samples uniformly from the dataset
        sampled_indices = np.random.choice(self.N, self.B, replace=False)
        x_sampled, y_sampled = self.dataset.train_data[sampled_indices]

        # Step 2: Compute scores for the sampled B samples
        scores_sampled = self.model.score(x_sampled, y_sampled, batch_size=self.B)
        scores_sampled = np.nan_to_num(scores_sampled, nan=0.0, posinf=1.0, neginf=0.0)
        scores_sampled += 1e-8  # To avoid division by zero

        # Step 3: Use quantum optimizer to select top K samples
        optimizer = GroverOptimizer(num_value_qubits=4, num_key_qubits=5, num_iterations=3)
        result = optimizer.solve(scores_sampled, self.K)
        top_k_indices = np.array(list(result.keys()))
        print(f"Selected top K indices via quantum optimizer: {top_k_indices}")
        print(f"Number of top K samples selected: {len(top_k_indices)}")

        if len(top_k_indices) == 0:
            print("Warning: Failed to get valid sample indices, falling back to random sampling.")
            return self._random_sample(batch_size)

        # Handle remaining samples with random sampling if needed
        num_selected = len(top_k_indices)
        num_other = batch_size - num_selected
        if num_other > 0:
            remaining_indices = np.setdiff1d(np.arange(self.B), top_k_indices)
            if len(remaining_indices) >= num_other:
                idxs_other = np.random.choice(remaining_indices, num_other, replace=False)
            else:
                idxs_other = np.random.choice(remaining_indices, num_other, replace=True)
        else:
            idxs_other = np.array([], dtype=int)

        # Combine high-score and random indices
        final_indices = top_k_indices
        if num_other > 0:
            final_indices = np.concatenate([top_k_indices, idxs_other])

        # Retrieve selected samples and their scores
        selected_indices = sampled_indices[final_indices]
        x_selected, y_selected = self._slice_data(self.dataset.train_data, self.dataset.train_labels, selected_indices)
        scores_selected = scores_sampled[final_indices]

        # Step 4: Compute weights for unbiasedness
        # Q(x) = B/N, selecting K samples from B uniformly via optimizer
        # Thus, P(x) = K/N (assuming target distribution is uniform P(x) = 1/N)
        # Hence, w(x) = P(x)/Q(x) = (K/N)/(B/N) = K/B
        weights = np.ones((len(final_indices), 1)) * (self.K / self.B)

        print(f"Computed weights (uniform for selected K samples): {weights.flatten()}")

        # Step 5: Update variance reduction condition
        self.condition.update(scores_selected)

        # Step 6: Send sampling messages
        self._send_messages(selected_indices, (x_selected, y_selected), weights, scores_selected)

        print(f"Returned batch shapes: x: {x_selected.shape}, y: {y_selected.shape}, w: {weights.shape}")
        return (selected_indices, (x_selected, y_selected), weights)
关键修改点说明
仅对B个样本计算得分：
通过sampled_indices = np.random.choice(self.N, self.B, replace=False)随机选取B个样本。
计算这B个样本的得分scores_sampled。
使用量子优化器选择K个高分样本：
使用量子优化器GroverOptimizer选择得分最高的K个样本。
如果选择失败，则回退到随机采样。
计算无偏权重：
由于我们从B个样本中选择K个，且每个样本被选中的概率为K/B。
为了使采样无偏，权重w(x) = K/B。
发送采样消息：
将选中的样本及其权重发送出去，供训练过程使用。
2. 修改 model_wrappers.py 文件
为了使模型能够接受并应用样本权重，需要调整模型包装器(OracleWrapper)。具体来说，我们需要确保损失函数接收并应用这些权重。

修改后的 model_wrappers.py

# model_wrappers.py

from functools import partial
import sys
import numpy as np
import tensorflow.keras.backend as K
from tensorflow.keras.layers import Input, Layer, multiply
from tensorflow.keras.models import Model, clone_model
from tensorflow.keras import losses
from blinker import signal

from .layers import GradientNormLayer, LossLayer, MetricLayer
from .reweighting import NoReweightingPolicy  # Using NoReweightingPolicy since weights are handled in sampler
from .utils.functional import _tolist, compose

class ModelWrapper(object):
    """ModelWrapper 的目的是包装一个神经网络，并添加一些额外的层，以生成得分、损失和样本权重，以执行重要性采样。"""

    def _iterate_batches(self, x, y, batch_size):
        bs = batch_size
        for s in range(0, len(y), bs):
            if isinstance(x, (list, tuple)):
                xs = [xi[s:s+bs] for xi in x]
            else:
                xs = x[s:s+bs]
            yield xs, y[s:s+bs]

    def evaluate(self, x, y, batch_size=128):
        result = np.mean(
            np.vstack([
                self.evaluate_batch(xi, yi)
                for xi, yi in self._iterate_batches(x, y, batch_size)
            ]),
            axis=0
        )
        signal("is.evaluation").send(result)
        return result

    def score(self, x, y, batch_size=128):
        bs = batch_size
        result = np.hstack([
            self.score_batch(xi, yi).T
            for xi, yi in self._iterate_batches(x, y, batch_size)
        ]).T
        signal("is.score").send(result)
        return result

    def set_lr(self, lr):
        """设置模型的学习率。"""
        try:
            K.set_value(
                self.optimizer.lr,
                lr
            )
        except AttributeError:
            try:
                K.set_value(
                    self.model.optimizer.lr,
                    lr
                )
            except AttributeError:
                raise NotImplementedError()

        try:
            K.set_value(
                self.small.optimizer.lr,
                lr
            )
        except AttributeError:
            pass

    def evaluate_batch(self, x, y):
        raise NotImplementedError()

    def score_batch(self, x, y):
        raise NotImplementedError()

    def train_batch(self, x, y, w):
        raise NotImplementedError()

class OracleWrapper(ModelWrapper):
    AVG_LOSS = 0
    LOSS = 1
    WEIGHTED_LOSS = 2
    SCORE = 3
    METRIC0 = 4

    FUSED_ACTIVATION_WARNING = ("[WARNING]: 最后一层具有融合激活函数，例如 Dense(..., activation=\"sigmoid\")。\n"
                                 "为了自动提取预激活张量，请使用单独的激活层（参见示例）。\n")

    def __init__(self, model, reweighting, score="loss", layer=None):
        self.reweighting = reweighting
        self.layer = self._gnorm_layer(model, layer)

        # Augment the model with reweighting, scoring etc
        # Save the new model and the training functions in member variables
        self._augment_model(model, score, reweighting)

    def _gnorm_layer(self, model, layer):
        # If we were given a layer then use it directly
        if isinstance(layer, Layer):
            return layer

        # If we were given a layer index extract the layer
        if isinstance(layer, int):
            return model.layers[layer]

        try:
            # Get the last or the previous to last layer depending on whether
            # the last has trainable weights
            skip_one = not bool(model.layers[-1].trainable_weights)
            last_layer = -2 if skip_one else -1

            # If the last layer has trainable weights that means that we cannot
            # automatically extract the preactivation tensor so we have to warn
            # them because they might be missing out or they might not even
            # have noticed
            if last_layer == -1:
                config = model.layers[-1].get_config()
                if config.get("activation", "linear") != "linear":
                    sys.stderr.write(self.FUSED_ACTIVATION_WARNING)

            return model.layers[last_layer]
        except:
            # In case of an error then probably we are not using the gnorm
            # importance
            return None

    def _augment_model(self, model, score, reweighting):
        # Extract some info from the model
        loss = model.loss
        optimizer = model.optimizer.__class__.from_config(model.optimizer.get_config())
        output_shape = model.get_output_shape_at(0)[1:]
        if isinstance(loss, str) and loss.startswith("sparse"):
            output_shape = output_shape[:-1] + (1,)

        # Make sure that some stuff look ok
        assert not isinstance(loss, list)

        # We need to create two more inputs
        #   1. the targets
        #   2. the predicted scores
        y_true = Input(shape=output_shape, name='y_true')
        pred_score = Input(shape=(reweighting.weight_size,), name='pred_score')

        # Create a loss layer and a score layer
        loss_tensor = LossLayer(loss)([y_true, model.get_output_at(0)])
        score_tensor = _get_scoring_layer(
            score,
            y_true,
            model.get_output_at(0),
            loss,
            self.layer,
            model
        )

        # Create the sample weights
        weights = reweighting.weight_layer()([score_tensor, pred_score])

        # Create the output
        weighted_loss = multiply([loss_tensor, weights])
        for l in model.losses:
            weighted_loss += l
        weighted_loss_mean = K.mean(weighted_loss)

        # Create the metric layers
        metrics = model.metrics or []
        metrics = [
            MetricLayer(metric)([y_true, model.get_output_at(0)])
            for metric in metrics
        ]

        # Create a model for plotting and providing access to things such as
        # trainable_weights etc.
        new_model = Model(
            inputs=_tolist(model.get_input_at(0)) + [y_true, pred_score],
            outputs=[weighted_loss_mean]
        )

        # Build separate on_batch keras functions for scoring and training
        updates = optimizer.get_updates(
            weighted_loss_mean,
            new_model.trainable_weights
        )
        metrics_updates = []
        if hasattr(model, "metrics_updates"):
            metrics_updates = model.metrics_updates
        learning_phase = []
        if weighted_loss_mean._uses_learning_phase:
            learning_phase.append(K.learning_phase())
        inputs = _tolist(model.get_input_at(0)) + [y_true, pred_score] + learning_phase
        outputs = [
            weighted_loss_mean,
            loss_tensor,
            weighted_loss,
            score_tensor
        ] + metrics

        train_on_batch = K.function(
            inputs=inputs,
            outputs=outputs,
            updates=updates + model.updates + metrics_updates
        )
        evaluate_on_batch = K.function(
            inputs=inputs,
            outputs=outputs,
            updates=model.state_updates + metrics_updates
        )

        self.model = new_model
        self.optimizer = optimizer
        self.model.optimizer = optimizer
        self._train_on_batch = train_on_batch
        self._evaluate_on_batch = evaluate_on_batch

    def evaluate_batch(self, x, y):
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)
        dummy_weights = np.ones((y.shape[0], self.reweighting.weight_size))
        inputs = _tolist(x) + [y, dummy_weights] + [0]
        outputs = self._evaluate_on_batch(inputs)

        signal("is.evaluate_batch").send(outputs)

        return np.hstack([outputs[self.LOSS]] + outputs[self.METRIC0:])

    def score_batch(self, x, y):
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)
        dummy_weights = np.ones((y.shape[0], self.reweighting.weight_size))
        inputs = _tolist(x) + [y, dummy_weights] + [0]
        outputs = self._evaluate_on_batch(inputs)

        return outputs[self.SCORE].ravel()

    def train_batch(self, x, y, w):
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)

        # train on a single batch
        outputs = self._train_on_batch(_tolist(x) + [y, w, 1])

        # Add the outputs in a tuple to send to whoever is listening
        result = (
            outputs[self.WEIGHTED_LOSS],
            outputs[self.METRIC0:],
            outputs[self.SCORE]
        )
        signal("is.training").send(result)

        return result

def _tolist(x, acceptable_iterables=(list, tuple)):
    if not isinstance(x, acceptable_iterables):
        x = [x]
    return x

def _get_scoring_layer(score, y_true, y_pred, loss_fn="categorical_crossentropy",
                      layer=None, model=None):
    """获取一个计算每个 y_true 和 y_pred 对的得分的层"""
    assert score in ["loss", "gnorm", "full_gnorm", "acc"]

    if score == "loss":
        return LossLayer(loss_fn, name='score_layer')([
            y_true,
            y_pred
        ])
    elif score == "gnorm":
        return GradientNormLayer(
            layer.output,
            loss_fn,
            fast=True,
            name='score_layer'
        )([
            y_true,
            y_pred
        ])
    elif score == "full_gnorm":
        return GradientNormLayer(
            model.trainable_weights,
            loss_fn,
            fast=False,
            name='score_layer'
        )([
            y_true,
            y_pred
        ])
    elif score == "acc":
        return MetricLayer("accuracy", name='score_layer')([
            y_true,
            y_pred
        ])
关键修改点说明
ConstantTimeSampler 类修改：
参数：
添加参数 B（每批次采样的样本数）和 K（从B个样本中选择的样本数）。
采样流程：
Step 1：从整个数据集中随机均匀采样 B 个样本。
Step 2：计算这 B 个样本的得分。
Step 3：使用量子优化器 GroverOptimizer 从这 B 个样本中选择得分最高的 K 个样本。
Step 4：为选择的 K 个样本分配权重 w(x) = K / B 以确保无偏。
Step 5：更新条件以决定是否在下一个批次应用重要性采样。
无偏权重计算：
每个被选中的样本权重设置为 w(x) = K / B。这基于以下假设：
目标分布 P(x) = 1/N（均匀分布）。
采样分布 Q(x) = B/N（从N个样本中均匀采样B个）。
选择分布 P_select(x | B) = 1/K（从B个样本中均匀选择K个）。
因此，w(x) = P(x) / (Q(x) * P_select(x | B)) = (1/N) / ((B/N) * (1/K)) ) = K/B。
发送采样消息：
将被采样的K个样本及其权重发送出去，供训练过程使用。
2. 修改 model_wrappers.py 文件
确保模型包装器 (OracleWrapper) 能够正确接受和应用样本权重。假设 OracleWrapper 已设计为处理样本权重，我们需要确保其与修改后的采样器兼容。

修改后的 model_wrappers.py

# model_wrappers.py

from functools import partial
import sys
import numpy as np
import tensorflow.keras.backend as K
from tensorflow.keras.layers import Input, Layer, multiply
from tensorflow.keras.models import Model, clone_model
from tensorflow.keras import losses
from blinker import signal

from .layers import GradientNormLayer, LossLayer, MetricLayer
from .reweighting import NoReweightingPolicy  # Using NoReweightingPolicy since weights are handled in sampler
from .utils.functional import _tolist, compose

class ModelWrapper(object):
    """ModelWrapper 的目的是包装一个神经网络，并添加一些额外的层，以生成得分、损失和样本权重，以执行重要性采样。"""

    def _iterate_batches(self, x, y, batch_size):
        bs = batch_size
        for s in range(0, len(y), bs):
            if isinstance(x, (list, tuple)):
                xs = [xi[s:s+bs] for xi in x]
            else:
                xs = x[s:s+bs]
            yield xs, y[s:s+bs]

    def evaluate(self, x, y, batch_size=128):
        result = np.mean(
            np.vstack([
                self.evaluate_batch(xi, yi)
                for xi, yi in self._iterate_batches(x, y, batch_size)
            ]),
            axis=0
        )
        signal("is.evaluation").send(result)
        return result

    def score(self, x, y, batch_size=128):
        bs = batch_size
        result = np.hstack([
            self.score_batch(xi, yi).T
            for xi, yi in self._iterate_batches(x, y, batch_size)
        ]).T
        signal("is.score").send(result)
        return result

    def set_lr(self, lr):
        """设置模型的学习率。"""
        try:
            K.set_value(
                self.optimizer.lr,
                lr
            )
        except AttributeError:
            try:
                K.set_value(
                    self.model.optimizer.lr,
                    lr
                )
            except AttributeError:
                raise NotImplementedError()

        try:
            K.set_value(
                self.small.optimizer.lr,
                lr
            )
        except AttributeError:
            pass

    def evaluate_batch(self, x, y):
        raise NotImplementedError()

    def score_batch(self, x, y):
        raise NotImplementedError()

    def train_batch(self, x, y, w):
        raise NotImplementedError()

class OracleWrapper(ModelWrapper):
    AVG_LOSS = 0
    LOSS = 1
    WEIGHTED_LOSS = 2
    SCORE = 3
    METRIC0 = 4

    FUSED_ACTIVATION_WARNING = ("[WARNING]: 最后一层具有融合激活函数，例如 Dense(..., activation=\"sigmoid\")。\n"
                                 "为了自动提取预激活张量，请使用单独的激活层（参见示例）。\n")

    def __init__(self, model, reweighting, score="loss", layer=None):
        self.reweighting = reweighting
        self.layer = self._gnorm_layer(model, layer)

        # Augment the model with reweighting, scoring etc
        # Save the new model and the training functions in member variables
        self._augment_model(model, score, reweighting)

    def _gnorm_layer(self, model, layer):
        # If we were given a layer then use it directly
        if isinstance(layer, Layer):
            return layer

        # If we were given a layer index extract the layer
        if isinstance(layer, int):
            return model.layers[layer]

        try:
            # Get the last or the previous to last layer depending on whether
            # the last has trainable weights
            skip_one = not bool(model.layers[-1].trainable_weights)
            last_layer = -2 if skip_one else -1

            # If the last layer has trainable weights that means that we cannot
            # automatically extract the preactivation tensor so we have to warn
            # them because they might be missing out or they might not even
            # have noticed
            if last_layer == -1:
                config = model.layers[-1].get_config()
                if config.get("activation", "linear") != "linear":
                    sys.stderr.write(self.FUSED_ACTIVATION_WARNING)

            return model.layers[last_layer]
        except:
            # In case of an error then probably we are not using the gnorm
            # importance
            return None

    def _augment_model(self, model, score, reweighting):
        # Extract some info from the model
        loss = model.loss
        optimizer = model.optimizer.__class__.from_config(model.optimizer.get_config())
        output_shape = model.get_output_shape_at(0)[1:]
        if isinstance(loss, str) and loss.startswith("sparse"):
            output_shape = output_shape[:-1] + (1,)

        # Make sure that some stuff look ok
        assert not isinstance(loss, list)

        # We need to create two more inputs
        #   1. the targets
        #   2. the predicted scores
        y_true = Input(shape=output_shape, name='y_true')
        pred_score = Input(shape=(reweighting.weight_size,), name='pred_score')

        # Create a loss layer and a score layer
        loss_tensor = LossLayer(loss)([y_true, model.get_output_at(0)])
        score_tensor = _get_scoring_layer(
            score,
            y_true,
            model.get_output_at(0),
            loss,
            self.layer,
            model
        )

        # Create the sample weights
        weights = reweighting.weight_layer()([score_tensor, pred_score])

        # Create the output
        weighted_loss = multiply([loss_tensor, weights])
        for l in model.losses:
            weighted_loss += l
        weighted_loss_mean = K.mean(weighted_loss)

        # Create the metric layers
        metrics = model.metrics or []
        metrics = [
            MetricLayer(metric)([y_true, model.get_output_at(0)])
            for metric in metrics
        ]

        # Create a model for plotting and providing access to things such as
        # trainable_weights etc.
        new_model = Model(
            inputs=_tolist(model.get_input_at(0)) + [y_true, pred_score],
            outputs=[weighted_loss_mean]
        )

        # Build separate on_batch keras functions for scoring and training
        updates = optimizer.get_updates(
            weighted_loss_mean,
            new_model.trainable_weights
        )
        metrics_updates = []
        if hasattr(model, "metrics_updates"):
            metrics_updates = model.metrics_updates
        learning_phase = []
        if weighted_loss_mean._uses_learning_phase:
            learning_phase.append(K.learning_phase())
        inputs = _tolist(model.get_input_at(0)) + [y_true, pred_score] + learning_phase
        outputs = [
            weighted_loss_mean,
            loss_tensor,
            weighted_loss,
            score_tensor
        ] + metrics

        train_on_batch = K.function(
            inputs=inputs,
            outputs=outputs,
            updates=updates + model.updates + metrics_updates
        )
        evaluate_on_batch = K.function(
            inputs=inputs,
            outputs=outputs,
            updates=model.state_updates + metrics_updates
        )

        self.model = new_model
        self.optimizer = optimizer
        self.model.optimizer = optimizer
        self._train_on_batch = train_on_batch
        self._evaluate_on_batch = evaluate_on_batch

    def evaluate_batch(self, x, y):
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)
        dummy_weights = np.ones((y.shape[0], self.reweighting.weight_size))
        inputs = _tolist(x) + [y, dummy_weights] + [0]
        outputs = self._evaluate_on_batch(inputs)

        signal("is.evaluate_batch").send(outputs)

        return np.hstack([outputs[self.LOSS]] + outputs[self.METRIC0:])

    def score_batch(self, x, y):
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)
        dummy_weights = np.ones((y.shape[0], self.reweighting.weight_size))
        inputs = _tolist(x) + [y, dummy_weights] + [0]
        outputs = self._evaluate_on_batch(inputs)

        return outputs[self.SCORE].ravel()

    def train_batch(self, x, y, w):
        if len(y.shape) == 1:
            y = np.expand_dims(y, axis=1)

        # train on a single batch
        outputs = self._train_on_batch(_tolist(x) + [y, w, 1])

        # Add the outputs in a tuple to send to whoever is listening
        result = (
            outputs[self.WEIGHTED_LOSS],
            outputs[self.METRIC0:],
            outputs[self.SCORE]
        )
        signal("is.training").send(result)

        return result
关键修改点说明
OracleWrapper 类的 _augment_model 方法：
添加了 pred_score 输入，用于传递样本权重。
使用 multiply 层将损失与权重相乘，确保权重被正确应用。
计算加权后的平均损失 weighted_loss_mean。
无偏权重应用：
通过将权重直接传递给损失函数，确保训练过程基于计算好的无偏权重。
3. 修改 reweighting.py 文件
由于我们在采样器中已直接计算并分配权重（w(x) = K/B），此处可以使用 NoReweightingPolicy，即不对权重进行进一步调整。

修改后的 reweighting.py

# reweighting.py

from tensorflow.keras.layers import Layer
from tensorflow.keras import backend as K
import numpy as np

class ReweightingPolicy(object):
    """ReweightingPolicy defines how we weigh the samples given their
    importance.

    Each policy should provide
    1. A layer implementation for use with Keras models
    2. A python implementation for use with the samplers
    """
    def weight_layer(self):
        """Return a layer that accepts the scores and the return value of the
        sample_weights() method and produces new sample weights."""
        raise NotImplementedError()

    def sample_weights(self, idxs, scores):
        raise NotImplementedError()

    @property
    def weight_size(self):
        raise NotImplementedError()

class NoReweightingPolicy(ReweightingPolicy):
    """Set all sample weights to 1. In our case, weights are already handled in sampler."""
    def weight_layer(self):
        return ExternalReweighting()

    def sample_weights(self, idxs, scores):
        return np.ones((len(idxs), 1))  # This won't be used as weights are handled in sampler

    @property
    def weight_size(self):
        return 1

class ExternalReweighting(Layer):
    """Use the provided input as sample weights"""
    def build(self, input_shape):
        super(ExternalReweighting, self).build(input_shape)

    def compute_output_shape(self, input_shape):
        return input_shape[0]

    def call(self, x):
        return K.stop_gradient(x[1])
关键修改点说明
NoReweightingPolicy 类：
返回 ExternalReweighting 层，该层直接使用传递的样本权重，无需进一步调整。
ExternalReweighting 类：
该层直接传递样本权重，确保权重在损失计算中被正确应用。
4. 修改主训练脚本 wine_QNNimprove.py
确保训练过程中正确传递和使用样本权重。同时，调整训练逻辑以适应无偏采样。

修改后的 wine_QNNimprove.py

# wine_QNNimprove.py

import sys
import os
import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.decomposition import PCA
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.keras.layers import Dense
from importance_sampling.training import ConstantTimeImportanceTraining  # 确保该库已安装

plt.rcParams['font.sans-serif'] = ['SimHei']  # 使用黑体
plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题

# 重定向 stdout 以捕获打印输出
class Tee(object):
    def __init__(self, *files):
        self.files = files
    def write(self, obj):
        for f in self.files:
            f.write(obj)
            f.flush()
    def flush(self):
        for f in self.files:
            f.flush()

output_file = open('QNNImprove.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)

def save_plot(fig, filename):
    fig.savefig(filename)
    plt.close(fig)

def plot_history(history):
    # 绘制训练和验证准确率
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['accuracy'])
    ax.plot(history.history['val_accuracy'])
    ax.set_title('模型准确率')
    ax.set_ylabel('准确率')
    ax.set_xlabel('轮次')
    ax.legend(['训练', '验证'], loc='upper left')
    save_plot(fig, 'accuracy_plot.png')

    # 绘制训练和验证损失
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['loss'])
    ax.plot(history.history['val_loss'])
    ax.set_title('模型损失')
    ax.set_ylabel('损失')
    ax.set_xlabel('轮次')
    ax.legend(['训练', '验证'], loc='upper left')
    save_plot(fig, 'loss_plot.png')

def calculate_accuracy_fluctuation(history):
    val_accuracies = history.history['val_accuracy']
    mean_accuracy = np.mean(val_accuracies)
    fluctuation = np.sum(np.abs(val_accuracies - mean_accuracy))
    return mean_accuracy, fluctuation

# 定义设备，使用 8 个量子比特
n_qubits = 8  # 根据需要的特征数量调整量子比特数
dev = qml.device("default.qubit", wires=n_qubits)

def encode_features(inputs, n_qubits):
    """
    使用角度编码将输入特征编码到量子电路中。
    将特征映射到量子比特的旋转角度。
    """
    # 将特征映射到量子比特
    for i in range(n_qubits):
        qml.RY(inputs[i], wires=i)

@qml.qnode(dev, interface='tf', diff_method='backprop')
def quantum_neural_network(inputs, rot_weights):
    # 编码输入特征到量子比特
    encode_features(inputs, n_qubits)

    # 应用变分层
    for layer in range(rot_weights.shape[0]):
        for qubit in range(n_qubits):
            rot_angles = rot_weights[layer, qubit, :]  # 形状：(3,)
            qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)
        # 修改纠缠方式，使用全连接纠缠
        for i in range(n_qubits):
            for j in range(i + 1, n_qubits):
                qml.CRX(np.pi / 2, wires=[i, j])

    # 测量期望值
    return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

# 定义用于绘制电路的 QNode
@qml.qnode(dev)
def quantum_neural_network_draw(inputs, rot_weights):
    # 编码输入特征到量子比特
    encode_features(inputs, n_qubits)

    # 应用变分层
    for layer in range(rot_weights.shape[0]):
        for qubit in range(n_qubits):
            rot_angles = rot_weights[layer, qubit, :]  # 形状：(3,)
            qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)
        # 修改纠缠方式，使用全连接纠缠
        for i in range(n_qubits):
            for j in range(i + 1, n_qubits):
                qml.CRX(np.pi / 2, wires=[i, j])

    # 测量期望值
    return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

# 定义量子神经网络层
class QuantumNeuralNetworkLayer(tf.keras.layers.Layer):
    def __init__(self, n_qubits, n_layers, **kwargs):
        super(QuantumNeuralNetworkLayer, self).__init__(**kwargs)
        self.n_qubits = n_qubits
        self.n_layers = n_layers

        # 定义旋转权重
        init_weights = tf.random.uniform(
            shape=(n_layers, n_qubits, 3),
            minval=0,
            maxval=2 * np.pi,
            dtype=tf.float32
        )
        self.rot_weights = tf.Variable(
            initial_value=init_weights,
            trainable=True,
            name='rot_weights'
        )

    def call(self, inputs):
        def apply_qnode(x):
            x = tf.cast(x, dtype=tf.float32)
            result = quantum_neural_network(x, self.rot_weights)
            return tf.cast(result, dtype=tf.float32)

        outputs = tf.map_fn(
            apply_qnode,
            inputs,
            dtype=tf.float32
        )

        return outputs

# 定义量子神经网络模型
class QuantumNeuralNetwork(tf.keras.Model):
    def __init__(self, n_qubits, n_layers, num_classes, **kwargs):
        super(QuantumNeuralNetwork, self).__init__(**kwargs)
        self.n_qubits = n_qubits
        self.n_layers = n_layers
        self.num_classes = num_classes

        # 量子层
        self.quantum_layer = QuantumNeuralNetworkLayer(n_qubits, n_layers)

        # 经典输出层
        self.output_layer = tf.keras.layers.Dense(num_classes, activation='softmax')

    def call(self, inputs):
        q_output = self.quantum_layer(inputs)
        return self.output_layer(q_output)

if __name__ == "__main__":
    # **重要性采样始终启用**

    # 加载 Wine 数据集
    wine = load_wine()
    X = wine['data'].astype(np.float32)
    y = wine['target'].reshape(-1, 1)

    # One-hot 编码目标标签
    encoder = OneHotEncoder(sparse_output=False)
    y_encoded = encoder.fit_transform(y).astype(np.float32)
    num_classes = y_encoded.shape[1]

    # 先划分数据集
    x_train, x_test, y_train, y_test = train_test_split(
        X, y_encoded, test_size=0.3, random_state=42
    )

    # 特征标准化 (仅在训练集上拟合)
    scaler = StandardScaler()
    x_train_scaled = scaler.fit_transform(x_train)
    x_test_scaled = scaler.transform(x_test)

    # 使用 PCA 进行降维 (仅在训练集上拟合)
    n_features = n_qubits  # 将特征数量降至与量子比特数量一致
    pca = PCA(n_components=n_features)
    x_train_reduced = pca.fit_transform(x_train_scaled)
    x_test_reduced = pca.transform(x_test_scaled)

    # 将特征归一化到 [0, π] (基于训练集的最小值和最大值)
    x_min = x_train_reduced.min(axis=0)
    x_max = x_train_reduced.max(axis=0)
    x_train_normalized = (x_train_reduced - x_min) / (x_max - x_min) * np.pi
    x_test_normalized = (x_test_reduced - x_min) / (x_max - x_min) * np.pi

    x_train = x_train_normalized
    x_test = x_test_normalized

    learning_rate = 0.01  # 学习率
    epochs = 200  # 训练轮次
    batch_size = 16
    n_layers = 5  # 增加量子层数
    B = 128  # 采样子集大小
    K = 16  # 每批选取的样本数

    model = QuantumNeuralNetwork(
        n_qubits=n_qubits,
        n_layers=n_layers,
        num_classes=num_classes
    )

    # 使用 Adam 优化器编译模型
    model.compile(
        optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),
        loss='categorical_crossentropy',
        metrics=['accuracy']
    )

    def is_quantum_model(model):
        for layer in model.layers:
            if isinstance(layer, QuantumNeuralNetworkLayer):
                return True
        return False

    if is_quantum_model(model):
        print("模型包含量子神经网络（QNN）层。")
    else:
        print("模型不包含量子神经网络（QNN）层。")

    print("\n量子电路图：")
    sample_inputs = np.ones(n_qubits, dtype=np.float32) * (np.pi / 2)  # 使用 n_qubits 个特征
    sample_rot_weights = np.zeros((n_layers, n_qubits, 3), dtype=np.float32)  # rot_weights

    print(qml.draw(quantum_neural_network_draw)(sample_inputs, sample_rot_weights))  # 使用绘制专用的 QNode

    # 早停和学习率调整回调函数
    callbacks = [
        tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True),
        tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=15)
    ]

    # **始终使用重要性采样进行训练**
    reweighting_policy = NoReweightingPolicy()  # 使用无重加权策略
    wrapped_model = ConstantTimeImportanceTraining(model, reweighting=reweighting_policy, B=B, K=K)

    history = wrapped_model.fit(
        x_train, y_train,
        batch_size=batch_size,
        epochs=epochs,
        verbose=1,
        validation_data=(x_test, y_test),
        callbacks=callbacks
    )

    score = model.evaluate(x_test, y_test, verbose=0)
    print('测试损失:', score[0])
    print('测试准确率:', score[1])

    mean_accuracy, fluctuation = calculate_accuracy_fluctuation(history)
    print('平均验证准确率:', mean_accuracy)
    print('准确率波动:', fluctuation)

    plot_history(history)

    # 关闭输出文件并恢复 stdout
    sys.stdout = original_stdout
    output_file.close()

    print("脚本执行完成。查看 'QNNImprove.log' 获取打印输出，查看当前目录获取保存的图表。")
关键修改点说明
采样器参数调整：
增加参数 B 和 K，分别表示每批次采样的子集大小和从子集中选择的样本数。
在实例化 ConstantTimeImportanceTraining 时，传递 B 和 K 参数。
无偏权重应用：
采样器 ConstantTimeSampler 已在 samplers.py 中计算并分配权重 w(x) = K/B。
在包装器 OracleWrapper 中，这些权重被正确接收并应用于损失函数。
日志与调试：
通过 print 语句和日志文件 QNNImprove.log 监控采样和权重分配过程，确保采样过程按预期工作。
训练过程：
使用修改后的 wrapped_model.fit 进行训练，传递样本权重供损失函数使用。
3. 其他必要的修改
确保辅助函数和类的定义在项目中正确无误。例如，在 layers.py 等文件中定义的类应保持不变，除非需要调整。

layers.py 文件保持不变
假设 layers.py 文件中的类（如 GradientNormLayer, LossLayer, MetricLayer 等）已正确定义并兼容修改后的采样器与模型包装器，我们无需对其进行进一步修改。

4. 总结与注意事项
总结
通过上述修改，我们实现了以下目标：

无偏采样：通过在采样器中仅计算部分样本的得分，并使用量子优化器选择高分样本，同时为这些样本分配权重 w(x) = K/B，确保采样过程无偏。
提高效率：避免每轮计算整个数据集的得分，仅对一部分子集（B个样本）进行评分，显著减少计算开销。
保持量子优化器不变：量子优化器 GroverOptimizer 的使用方式保持不变，仅在采样器中新增了权重计算与应用步骤。
文件兼容性：通过修改 samplers.py 和 model_wrappers.py，并在主训练脚本中正确传递参数，使各部分文件协调工作，实现无偏重要性采样。
注意事项
采样子集大小（B）的选择：
B 的值需要根据数据集大小和计算资源进行调整。较大的 B 可以提供更丰富的得分信息，但会增加计算开销。
可以通过实验确定最优的 B 值，以平衡效率与采样质量。
量子优化器的性能：
量子优化器 GroverOptimizer 的性能直接影响训练效率。确保其实现足够高效，适应 B 的大小。
权重计算的一致性：
确保采样器分配的权重与模型包装器正确配合，避免权重计算错误导致训练过程有偏。
模型的损失函数与权重应用：
确保损失函数正确应用了样本权重。在 OracleWrapper 中，weighted_loss 通过 multiply 层与权重相乘，确保权重被正确应用。
调试与验证：
在大规模训练前，使用小规模数据集进行调试，确保无偏采样与权重应用的正确性。
通过日志和可视化工具（如绘制训练历史）监控训练过程，确保模型性能稳定提升。
优化采样频率：
当前设置每 update_interval 个epoch更新一次得分。根据数据集和训练需求，可调整这一频率以优化训练效率与采样质量。
通过以上修改，项目1 现已具备高效且无偏的重要性采样能力，结合量子优化器，实现稳健且高效的模型训练。


仔细阅读上述内容，告诉我是否权重算错了，因为我的量子优化器基本能够肯定的从N个样本中选取出score最大的K个样本。如果没有算错，你需要解释一下为什么这样计算