Spaces:
Sleeping
Sleeping
File size: 2,845 Bytes
46e0dd0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import numpy as np
import torch
from torch.utils.data import Dataset
import json
import sys
sys.path.append("../")
from datasets.dataset_loader import SpatialDataset
from transformers import RobertaTokenizer, BertTokenizer
class WHGDataset(SpatialDataset):
# initializes dataset loader and converts dataset python object
def __init__(self, data_file_path, tokenizer=None,max_token_len = 512, distance_norm_factor = 1, spatial_dist_fill=100, sep_between_neighbors = False):
if tokenizer is None:
self.tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
else:
self.tokenizer = tokenizer
self.read_data(data_file_path)
self.max_token_len = max_token_len
self.distance_norm_factor = distance_norm_factor
self.spatial_dist_fill = spatial_dist_fill
self.sep_between_neighbors = sep_between_neighbors
# returns a specific item from the dataset given an index
def __getitem__(self, idx):
return self.load_data(idx)
# returns the length of the dataset loaded
def __len__(self):
return self.len_data
def get_average_distance(self,idx):
line = self.data[idx]
line_data_dict = json.loads(line)
pivot_pos = line_data_dict['info']['geometry']['coordinates']
neighbor_geom_list = line_data_dict['neighbor_info']['geometry_list']
lat_diff = 0
lng_diff = 0
for neighbor in neighbor_geom_list:
coordinates = neighbor['coordinates']
lat_diff = lat_diff + (abs(pivot_pos[0]-coordinates[0]))
lng_diff = lng_diff + (abs(pivot_pos[1]-coordinates[1]))
avg_lat_diff = lat_diff/len(neighbor_geom_list)
avg_lng_diff = lng_diff/len(neighbor_geom_list)
return (avg_lat_diff, avg_lng_diff)
# reads dataset from given filepath, run on initilization
def read_data(self, data_file_path):
with open(data_file_path, 'r') as f:
data = f.readlines()
len_data = len(data)
self.len_data = len_data
self.data = data
# loads and parses dataset
def load_data(self, idx):
line = self.data[idx]
line_data_dict = json.loads(line)
# get pivot info
pivot_name = str(line_data_dict['info']['name'])
pivot_pos = line_data_dict['info']['geometry']['coordinates']
# get neighbor info
neighbor_info = line_data_dict['neighbor_info']
neighbor_name_list = neighbor_info['name_list']
neighbor_geom_list = neighbor_info['geometry_list']
parsed_data = self.parse_spatial_context(pivot_name, pivot_pos, neighbor_name_list, neighbor_geom_list, self.spatial_dist_fill)
parsed_data['qid'] = line_data_dict['info']['qid']
return parsed_data
|