import lightgbm as lgb
import numpy as np
from scipy.linalg import qr
from progress.bar import Bar
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score


class SPA_acc:
    def _projections_qr(self, X, k, M):
        X_projected = X.copy()
        norms = np.sum((X ** 2), axis=0)
        norm_max = np.amax(norms)
        X_projected.iloc[:, k] = X_projected.iloc[:, k] * 2 * norm_max / norms[k]
        _, __, order = qr(X_projected.to_numpy(), 0, pivoting=True)
        return order[:M].T

    def _validation(self, Xcal, ycal, var_sel, Xval=None, yval=None):
        train_data = lgb.Dataset(Xcal.iloc[:, var_sel], label=ycal)
        params = {
            'objective': 'multiclass',  # 多分类任务的目标函数
            'boosting_type': 'gbdt',
            'metric': 'multi_logloss',  # 使用多分类交叉熵损失
            'device': 'gpu',  # 启用GPU加速
            'verbosity': -1,
            'num_class': len(np.unique(ycal))  # 设置类别数量
        }

        # LightGBM 模型训练
        model = lgb.train(params, train_data, num_boost_round=100)

        # 在验证集或训练集上进行预测
        if Xval is not None and yval is not None:
            yhat = model.predict(Xval.iloc[:, var_sel])
            yhat = np.argmax(yhat, axis=1)  # 获取每个样本的最大概率类别
            accuracy = accuracy_score(yval, yhat)
        else:
            yhat = model.predict(Xcal.iloc[:, var_sel])
            yhat = np.argmax(yhat, axis=1)  # 获取每个样本的最大概率类别
            accuracy = accuracy_score(ycal, yhat)

        return yhat, accuracy

    def spa(self, Xcal, ycal, m_min=1, m_max=None, Xval=None, yval=None, autoscaling=1, save_path=None):
        N, K = Xcal.shape
        m_max = min(N - 1, K) if m_max is None else m_max

        normalization_factor = Xcal.std(ddof=1, axis=0) if autoscaling else np.ones(K)
        Xcaln = (Xcal - Xcal.mean()) / normalization_factor

        SEL = np.zeros((m_max, K))
        with Bar('Projections :', max=K) as bar:
            for k in range(K):
                SEL[:, k] = self._projections_qr(Xcaln, k, m_max)
                bar.next()

        ACCURACY = np.full((m_max + 1, K), -np.inf)
        with Bar('Evaluating subsets:', max=K * (m_max - m_min + 1)) as bar:
            for k in range(K):
                for m in range(m_min, m_max + 1):
                    var_sel = SEL[:m, k].astype(int)
                    _, accuracy = self._validation(Xcal, ycal, var_sel, Xval, yval)
                    ACCURACY[m, k] = accuracy
                    bar.next()

        m_sel = np.argmax(ACCURACY, axis=0)
        k_sel = np.argmax(np.max(ACCURACY, axis=0))
        var_sel_phase2 = SEL[:m_sel[k_sel], k_sel].astype(int)

        # Final LightGBM Training
        Xcal2 = Xcal.iloc[:, var_sel_phase2]
        train_data = lgb.Dataset(Xcal2, label=ycal)
        params = {
            'objective': 'multiclass',  # 多分类任务的目标函数
            'boosting_type': 'gbdt',
            'metric': 'multi_logloss',  # 使用多分类交叉熵损失
            'device': 'gpu',  # 启用GPU加速
            'verbosity': -1,
            'num_class': len(np.unique(ycal))  # 设置类别数量
        }
        model = lgb.train(params, train_data, num_boost_round=100)
        relev = model.feature_importance(importance_type='gain')

        index_decreasing_relev = np.argsort(-relev)
        ACCURACY_scree = np.empty(len(var_sel_phase2))
        for i in range(len(var_sel_phase2)):
            var_sel = var_sel_phase2[index_decreasing_relev[:i + 1]]
            _, accuracy = self._validation(Xcal, ycal, var_sel, Xval, yval)
            ACCURACY_scree[i] = accuracy

        # 绘图
        plt.figure()
        plt.rcParams['font.sans-serif'] = ['Times New Roman']
        plt.xlabel('Number of variables included in the model', fontsize=14)
        plt.ylabel('Accuracy', fontsize=14)
        plt.title(f'Final number of selected variables: {len(var_sel_phase2)} (Accuracy={ACCURACY_scree.max():.4f})', fontsize=16)
        plt.plot(ACCURACY_scree, label='Accuracy Scree Plot')
        plt.scatter(np.argmax(ACCURACY_scree), ACCURACY_scree.max(), color='r', marker='s', label='Selected Point')
        plt.grid(True)
        plt.legend()

        if save_path:
            plt.savefig(save_path, bbox_inches='tight', dpi=300)
            print(f"图像已保存至: {save_path}")
        else:
            plt.show()

        return var_sel_phase2, ACCURACY_scree

    def __repr__(self):
        return "SPA()"