# 将数据点和MD一起聚类
import os
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt

from draw_md_cluster import DBSCAN
from ml_er.ml_entity_resolver import build_col_pairs_sim_tensor_dict


def plot(md_keys_, md_data_, pre_match_points_, pre_mismatch_points_, labels_, output_path_):
    clusterNum = len(set(labels_))
    fig = plt.figure()
    scatterColors = ['black', 'blue', 'green', 'yellow', 'red', 'purple', 'orange', 'brown']
    ax = fig.add_subplot(111, projection='3d')
    for i in range(-1, clusterNum):
        colorStyle = scatterColors[i % len(scatterColors)]
        subCluster = md_data_[np.where(labels_ == i)]
        ax.scatter(subCluster[:, 0], subCluster[:, 1], subCluster[:, 2], c=colorStyle, s=12)
    ax.scatter(pre_match_points_[:, 0], pre_match_points_[:, 1], pre_match_points_[:, 2], c='#66CCFF', s=12, marker='x')
    if pre_mismatch_points_.shape[0] > 0:
        ax.scatter(pre_mismatch_points_[:, 0], pre_mismatch_points_[:, 1], pre_mismatch_points_[:, 2], c='#006666', s=12, marker='x')
    ax.set_xlabel(md_keys_[0], rotation=0)  # 设置标签角度
    ax.set_ylabel(md_keys_[1], rotation=-45)
    ax.set_zlabel(md_keys_[2], rotation=0)
    plt.title(output_path_.split('\\')[-1].split('.')[0])
    plt.savefig(output_path_, dpi=500)
    plt.show()


if __name__ == '__main__':
    outcome_path = r'E:\Data\Research\Outcome'
    config_dir = r'\Magellan+Smac+roberta-large-nli-stsb-mean-tokens+inter-0.5'
    dataset_name_list = [f.name for f in os.scandir(outcome_path) if f.is_dir()]
    for dataset_name in dataset_name_list:
        absolute_path = outcome_path + rf'\{dataset_name}' + config_dir + r'\mds.txt'  # MD路径
        predictions = outcome_path + rf'\{dataset_name}' + config_dir + r'\predictions.csv'  # prediction路径
        pred = pd.read_csv(predictions)
        pred = pred.astype(str)
        # pred = pred[pred['predicted'] == str(1)]
        sim_tensor_dict = build_col_pairs_sim_tensor_dict(pred)
        # 选取的三个字段
        md_keys = []
        with open(absolute_path, 'r') as f:
            # 读取每一行的md，加入该文件的md列表
            md_data = []
            for line in f.readlines():
                md_metadata = line.strip().split('\t')
                md_tuple = eval(md_metadata[1])
                md_keys = list(md_tuple[0].keys())[1:4]
                md_values = list(md_tuple[0].values())
                md_data.append(md_values[1:4])
                if len(md_data) == 10000:
                    break

        pre_match_points = []
        pre_mismatch_points = []
        for _ in pred.itertuples():
            data_point_value = []
            for key in md_keys:
                sim_tensor = sim_tensor_dict[key]
                data_point_value.append(round(float(sim_tensor[_[0]]), 4))
            if getattr(_, 'predicted') == str(1):
                pre_match_points.append(data_point_value)
            elif getattr(_, 'predicted') == str(0):
                pre_mismatch_points.append(data_point_value)

        md_data = np.array(md_data, dtype=np.float32)
        pre_match_points = np.array(pre_match_points, dtype=np.float32)
        pre_mismatch_points = np.array(pre_mismatch_points, dtype=np.float32)
        labels = DBSCAN(md_data, 0.5, 30)
        output_path = outcome_path + rf'\{dataset_name}_MD&data.png'
        plot(md_keys, md_data, pre_match_points, pre_mismatch_points, labels, output_path)