matching_dependency/md_discovery/discovery_executor_gpu.py

import math
import operator
import random
import time
from tqdm import tqdm
import numpy as np
import pandas as pd
import torch

from settings import model, similarity_threshold, support_threshold, confidence_threshold, md_output_dir

sample_number = 100000
step_length = 0.01

def get_metrics(md_tensor, data, sim_tensor, target_index):
    length = data.shape[0]
    width = data.shape[1]

    # md_tensor = list(current_md.values())
    # md_tensor = torch.tensor(md_tensor, device='cuda')
    md_tensor_2d = md_tensor.unsqueeze(1)
    md_tensor_3d = md_tensor_2d.unsqueeze(2)
    md_tensor_3d = md_tensor_3d.repeat(1, length, length)

    sim_tensor = torch.round(sim_tensor, decimals=4)

    sup_tensor = torch.ge(sim_tensor, md_tensor_3d)
    ini_slice = torch.ones((length, length), dtype=torch.bool, device='cuda')
    for i in range(0, width):
        if i != target_index:
            sup_tensor_slice = sup_tensor[i]
            ini_slice = torch.logical_and(ini_slice, sup_tensor_slice)
    sup_tensor_int = ini_slice.int()
    support_Naumann = torch.count_nonzero(sup_tensor_int).item()
    support_Naumann = (support_Naumann - length) / 2

    conf_tensor = torch.logical_and(ini_slice, sup_tensor[target_index])
    conf_tensor_int = conf_tensor.int()
    support_Fan = torch.count_nonzero(conf_tensor_int).item()
    support_Fan = (support_Fan - length) / 2
    confidence = support_Fan / support_Naumann if support_Naumann > 0 else 0

    return support_Fan, confidence


def build_cartesian(width, target_index):
    all_values_array = np.linspace(start=similarity_threshold, stop=1, endpoint=True,
                                   num=math.ceil((1-similarity_threshold)/step_length) + 1)
    all_values_array = np.round(all_values_array, 4)
    all_values_tensor = torch.tensor(all_values_array, device='cuda')
    all_values_tensor = all_values_tensor.float()
    all_values_tensor = torch.round(all_values_tensor, decimals=4)
    tensors_for_cartesian = []
    for i in range(0, width):
        if i == target_index:
            t = torch.tensor([1.0], device='cuda')
            tensors_for_cartesian.append(t)
        else:
            tensors_for_cartesian.append(all_values_tensor)
    result = torch.cartesian_prod(*tensors_for_cartesian)
    return result


def get_metric_tensor(cartesian_product, data, sim_tensor, target_index):
    length = data.shape[0]
    width = data.shape[1]
    cartesian_product = cartesian_product.unsqueeze(2)
    cartesian_product = cartesian_product.unsqueeze(3)
    cartesian_product = cartesian_product.repeat(1, 1, length, length)


def discover(path, target_col):
    data = pd.read_csv(path, low_memory=False, encoding='ISO-8859-1')
    data.fillna("", inplace=True)
    data = data.astype(str)
    columns = data.columns.values.tolist()
    target_index = columns.index(target_col)
    cols_but_target = list(set(columns) - {target_col})
    length = data.shape[0]
    width = data.shape[1]
    # 除了目标列外所有列的索引
    columns_indices = [_ for _ in range(0, width) if _ != target_index]

    sentences = []
    for col in range(0, width):
        for row in range(0, length):
            cell_value = data.values[row, col]
            sentences.append(cell_value)
    embedding = model.encode(sentences, convert_to_tensor=True, device="cuda")
    split_embedding = torch.split(embedding, length, dim=0)
    table_tensor = torch.stack(split_embedding, dim=0, out=None)
    norm_table_tensor = torch.nn.functional.normalize(table_tensor, dim=2)
    sim_tensor = torch.matmul(norm_table_tensor, norm_table_tensor.transpose(1, 2))
    sim_tensor = sim_tensor.float()
    sim_tensor = torch.round(sim_tensor, decimals=4)

    # 小于6列的可以尝试做笛卡尔积，大于6列可能指数爆炸
    if width <= 6:
        # 列出除目标列以外所有列的所有取值，做笛卡尔积，结果为所有可能MD取值
        cartesian = build_cartesian(width, target_index)
        # 抽取sample_number / (width - 1)条MD，不含-1
        if cartesian.shape[0] > sample_number / (width - 1):
            index = torch.tensor(random.sample(range(cartesian.shape[0]), math.ceil(sample_number / (width - 1))), device='cuda')
            cartesian = torch.index_select(cartesian, 0, index)
    else:
        # 随机生成sample_number / (width - 1)条MD，使用randint先转化为int再除成小数，不含-1
        cartesian = torch.randint(int(similarity_threshold * 100), 1 * 100,
                                  (math.ceil(sample_number / (width - 1)), width - 1), device='cuda')
        cartesian = cartesian / 100
        # 生成一列相似度为1的目标列，插入目标列所在位置
        ones = torch.ones((math.ceil(sample_number / (width - 1)), 1), device='cuda')
        cartesian = torch.cat((cartesian[:, 0:target_index], ones, cartesian[:, target_index:]), 1)
        cartesian = torch.round(cartesian, decimals=4)
    # 此tensor将与其他置为-1的tensor拼接
    joint_md_tensor = cartesian.clone()
    # 随机将1列，2列……置为-1
    for i in range(width - 2):
        index_list_format = []
        for j in range(cartesian.shape[0]):
            # 对每条MD，随机选择将要置为-1的列索引
            index_list_format.append(random.sample(columns_indices, i + 1))
        index = torch.tensor(index_list_format, device='cuda')
        # 随机调整为-1后的MD集合
        modified_cartesian = cartesian.scatter(1, index, -1)
        joint_md_tensor = torch.cat((joint_md_tensor, modified_cartesian), 0)

    md_list = []
    # get_metric_tensor(cartesian, data, sim_tensor, target_index)
    for _ in tqdm(range(joint_md_tensor.shape[0])):
        s, c = get_metrics(joint_md_tensor[_], data, sim_tensor, target_index)
        if s >= support_threshold and c >= confidence_threshold:
            md_list_format = [round(i, 4) for i in joint_md_tensor[_].tolist()]
            md_dict_format = {}
            for k in range(0, width):
                md_dict_format[columns[k]] = md_list_format[k]
            md_list.append((md_dict_format, s, c))
    md_list.sort(key=operator.itemgetter(2), reverse=True)
    return md_list