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613ee99 | 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 | import pandas as pd
import numpy as np
import os
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import KFold
# from random import shuffle
import pyarrow.feather as feather
# pro_name = ['cesm', 'cnrm', 'csiro', 'fesom', 'ipsl', 'mpi', 'noresm', 'planktom', 'princeton', 'recom']
# pro_name = ['cesm', 'cnrm', 'csiro', 'fesom', 'ipsl', 'mpi', 'noresm', 'planktom', 'princeton', 'recom','xco2','ice','sst']
# pro_name = ['NorESM-OCv1.2','CESM2', 'Princeton', 'NEMO-PlankTOM12', 'MRI-ESM2-1', 'CESM-ETHZ','MPIOM-HAMOCC6', 'CNRM', 'IPSL', 'FESOM-2.1-REcoM2','sss', 'ssh','slp', 'wind', 'mld']
class OceanDataset_V2(Dataset):
def __init__(self, data, indexes, config, mode):
self.config = config
self.lat_patch = config['patch']
self.lon_patch = config['patch']
self.window_size = config['window']
indexes = np.array(indexes)
if mode == 'train':
indexes = self.select_top_rows(data, indexes, config['percent'])
data[..., :-1][np.isnan(data[..., :-1])] = 0
# data = np.nan_to_num(data[..., :-1], nan=0)
self.data = torch.from_numpy(data.astype(np.float32))
self.indexes = torch.from_numpy(indexes)
print(f'{mode} dataset shape: {self.data.shape}, indexes shape: {self.indexes.shape}')
def select_top_rows(self, data, indexes, percent):
if percent == 1:
return indexes
nan_rate = self.get_nan_rate(data, indexes)
top_row_idx = np.argsort(nan_rate)
top_row_idx = top_row_idx[:int(top_row_idx.shape[0] * percent)]
indexes = indexes[top_row_idx]
return indexes
def get_nan_rate(self, data, indexes):
labels = [self.get_label(data, d3_index) for d3_index in indexes]
labels = np.stack(labels, axis = 0).reshape(len(labels), -1)
nan_rate = np.isnan(labels).astype('float')
nan_rate = nan_rate.sum(axis=-1) / labels.shape[-1]
return nan_rate
def get_label(self, data, d3_index):
latitude_start, longitude_start, month_start = d3_index
label = data[latitude_start:latitude_start + self.lat_patch,
longitude_start:longitude_start + self.lon_patch,
month_start + self.window_size - 1, -1]
return label
def __getitem__(self, index):
latitude_start, longitude_start, month_start = self.indexes[index]
data = self.data[latitude_start:latitude_start + self.lat_patch,
longitude_start:longitude_start + self.lon_patch,
month_start:month_start + self.window_size, ...].clone()
if self.config['add_history_target']:
data[:, :, -1, -1] = 0
else:
data = data[..., :-1]
data = torch.nan_to_num(data, nan=0.0)
label = self.get_label(self.data, self.indexes[index]).unsqueeze(-1)
torch.isnan(label)
return data, label
def __getitem_new__(self, index):
latitude_start, longitude_start, month_start = self.indexes[index]
data = self.data[latitude_start:latitude_start + self.lat_patch,
longitude_start:longitude_start + self.lon_patch,
month_start:month_start + self.window_size, ...].clone()
# if self.config['add_history_target']:
# data[:, :, -1, -1] = 0
# else:
# data = data[..., :-1]
data[:, :, :, -1] = 0
history_year = self.config['history_year']
# List of indices for the third dimension
indices = [month_start + i*(-12) for i in range(1, history_year+1)]
# print(month_start, month_start + self.window_size)
# print(indices)
# Use advanced indexing to get the slices
data_history = self.data[latitude_start:latitude_start + self.lat_patch,
longitude_start:longitude_start + self.lon_patch,
indices, ...].clone()
# print(data.shape, data_history.shape)
data = torch.cat((data, data_history), dim=2)
data = torch.nan_to_num(data, nan=0.0)
label = self.get_label(self.data, self.indexes[index]).unsqueeze(-1)
torch.isnan(label)
return data, label
def __len__(self):
# Returns the size of the dataset
return len(self.indexes)
def get_data(mode, config):
# get dataset
# get feature and nbp data
print('target:', config['target'])
# preprocessed_path = os.path.join(os.path.dirname(__file__), config['preprocessed_data_dir'], '{}.feather'.format(mode))
preprocessed_path = os.path.join(config['preprocessed_data_dir'], '{}.feather'.format(mode))
print('read preprocessed data')
# get preprocessed data from feather
data = feather.read_feather(preprocessed_path, memory_map=True)
# print(f"data.columns: {data.columns}")
data = data[data['year']>=1990][config['columns']]
data = optimize_floats(data)
# read pft data
# pft = feather.read_feather(os.path.join(os.path.dirname(__file__), config['pft_data_dir'], f"{config['target']}.feather"), memory_map=True)
target_col = data.pop(config['target'])
data[config['target']] = target_col
print(f"data.columns: {data.columns}")
return data
# return data
# def get_data(mode, config):
# # get dataset
# preprocessed_path = os.path.join(os.path.dirname(__file__), config['preprocessed_data_dir'], '{}.feather'.format(mode))
# if (not os.path.exists(preprocessed_path)):
# # generate data and preprocessing
# print('transfer {} data from csv to feather'.format(mode))
# data = pd.read_csv(os.path.join(config['split_data_dir'], '{}.csv'.format(mode)))
# data.to_feather(preprocessed_path)
# print('data preprocess finished! data shape:{}'.format(data.shape))
# else:
# print('read preprocessed data')
# # get preprocessed data from feather
# # data = pd.read_feather(preprocessed_path, memory_map=True)
# data = feather.read_feather(preprocessed_path, memory_map=True)
# # data = optimize_floats(data)
# return data[config['columns']]
# # return data
def optimize_floats(df):
float64_cols = df.select_dtypes(include=['float64']).columns
df[float64_cols] = df[float64_cols].astype('float32')
return df
def add_prefix(data, prefix_data, L):
prefix_data = prefix_data.sort_values(by=['latitude', 'longitude', 'year', 'month'])
prefix_data = prefix_data.groupby(['latitude', 'longitude'], group_keys=False).apply(
lambda x: x.iloc[-L:]
).reset_index()
data = pd.concat([prefix_data, data], axis=0)
return data
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