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import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from typing import Union, List, Tuple, Dict
class NRELComstock(Dataset):
def __init__(
self,
data_array: np.ndarray,
num_bldg: int = 12,
lookback: int = 12,
lookahead:int = 4,
normalize: bool = True,
dtype: torch.dtype = torch.float32,
mean: np.ndarray = None,
std: np.ndarray = None,
transformer: bool = True
):
if data_array.shape[0] < num_bldg:
raise ValueError('More buildings than present in file!')
else:
self.data = data_array[:num_bldg,:,:]
self.num_clients = num_bldg
# lookback and lookahead
self.lookback, self.lookahead = lookback, lookahead
# Calculate statistics
stacked = self.data.reshape(self.data.shape[0]*self.data.shape[1],self.data.shape[2])
if (mean is None) or (std is None): # statistics are not provided. Generate it from data
self.mean = stacked.mean(axis=0,keepdims=True)
self.std = stacked.std(axis=0,keepdims=True)
else: # statistics are provided. Use the provided statistics
self.mean = mean
self.std = std
if transformer:
# TRANSFORMER SPECIFIC: do not normalize date and time
self.mean[0,1], self.std[0,1] = 0., 1.
self.mean[0,2], self.std[0,2] = 0., 1.
self.ndata = (self.data-self.mean)/self.std # normalized data
# disambiguating between clients
len_per_client = self.data.shape[1] - lookback - lookahead + 1
self.total_len = len_per_client * self.num_clients
self.splits = np.array_split(np.arange(self.total_len),self.num_clients)
# save whether to normalize, and the data type
self.normalize = normalize
self.dtype = dtype
# if normalization is disabled, return to default statistics
if not self.normalize:
self.mean = np.zeros_like(self.mean)
self.std = np.ones_like(self.std)
def _client_and_idx(self, idx):
part_size = self.total_len // self.num_clients
part_index = idx // part_size
relative_position = idx % part_size
return relative_position, part_index
def __len__(self):
return self.total_len
def __getitem__(self, idx):
tidx, cidx = self._client_and_idx(idx)
if self.normalize:
x = self.ndata[cidx,tidx:tidx+self.lookback,:]
y = self.ndata[cidx,tidx+self.lookback+self.lookahead-1,[0]]
else:
x = self.data[cidx,tidx:tidx+self.lookback,:]
y = self.data[cidx,tidx+self.lookback+self.lookahead-1,[0]]\
# Future time and weather indices
fut_time = self.data[cidx,tidx+self.lookback:tidx+self.lookback+self.lookahead,1:3]
x,y,fut_time,= torch.tensor(x,dtype=self.dtype), torch.tensor(y,dtype=self.dtype), torch.tensor(fut_time,dtype=self.dtype)
return x,y,fut_time
def get_data_and_generate_train_val_test_sets(
data_array: np.ndarray, # data matrix of shape (num_bldg,num_time_points,num_features). NOTE that features 2 and 3 are categorical features to embed time indices
split_ratios: Union[List,Tuple], # 3-element list containing the ratios of train-val-test
dataset_kwargs: Tuple # ONLY include num_bldg, lookback, lookahead, normalize, dtype, and transformer keys. See NRELComstock definition above for details.
) -> Tuple[torch.utils.data.Dataset, torch.utils.data.Dataset, torch.utils.data.Dataset, np.ndarray, np.ndarray]:
assert len(split_ratios) == 3, "The split list must contain three elements."
assert all(isinstance(i, (int, float)) for i in split_ratios), "List contains non-numeric elements"
assert sum(split_ratios) <= 1, "Ratios must not sum upto more than 1."
cum_splits = np.cumsum(split_ratios)
train = data_array[:,:int(cum_splits[0]*data_array.shape[1]),:]
val = data_array[:,int(cum_splits[0]*data_array.shape[1]):int(cum_splits[1]*data_array.shape[1]),:]
test = data_array[:,int(cum_splits[1]*data_array.shape[1]):,:]
if 0 in train.shape:
train_set = None
raise ValueError("Train set is empty. Possibly empty data matrix or 0 ratio for train set has been input.")
else:
train_set = NRELComstock(
data_array = train,
**dataset_kwargs
)
mean, std = train_set.mean, train_set.std
if 0 in val.shape:
val_set = None
else:
val_set = NRELComstock(
data_array = val,
mean = mean,
std = std,
**dataset_kwargs
)
if 0 in test.shape:
test_set = None
else:
test_set = NRELComstock(
data_array = test,
mean = mean,
std = std,
**dataset_kwargs
)
# return the three datasets, as well as the statistics
return train_set, val_set, test_set, mean, std |