File size: 7,172 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 | 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(Dataset):
def __init__(self, inputs, outputs, config, mode):
inputs, outputs = self.slice_patch(inputs, outputs, patch_size=config['patch'])
if mode == 'train':
inputs, outputs = self.select_top_rows(inputs, outputs, config['percent'])
self.data = torch.from_numpy(inputs).float()
self.target = torch.from_numpy(outputs).float()
def select_top_rows(self, inputs, outputs, percent):
nan_rate = self.get_nan_rate(outputs)
top_row_idx = np.argsort(nan_rate)
top_row_idx = top_row_idx[:int(top_row_idx.shape[0] * percent)]
inputs, outputs = inputs[top_row_idx], outputs[top_row_idx]
return inputs, outputs
def get_nan_rate(self, outputs):
outputs = outputs.reshape(outputs.shape[0], -1)
nan_rate = np.isnan(outputs).astype('float')
nan_rate = nan_rate.sum(axis=-1) / outputs.shape[-1]
return nan_rate
def slice_patch(self, inputs, outputs, patch_size=18):
B, H, W, L, _ = inputs.shape
patch_inputs = []
patch_outputs = []
stride = patch_size // 2
for i in range(0, H - patch_size+1, stride):
for j in range(0, W - patch_size+1, stride):
_inputs = inputs[:, i: i+patch_size, j: j+patch_size]
_outputs = outputs[:, i: i+patch_size, j: j+patch_size]
patch_inputs.append(_inputs)
patch_outputs.append(_outputs)
patch_inputs = np.concatenate(patch_inputs, axis=0)
patch_outputs = np.concatenate(patch_outputs, axis=0)
return patch_inputs, patch_outputs
def __getitem__(self, index):
return self.data[index], self.target[index]
def __len__(self):
# Returns the size of the dataset
return len(self.data)
def dataloader(data, config, shuffle, n_jobs=0, mode='train', train_shuffle=True, valid_shuffle=False):
''' Generates a dataset, then is put into a dataloader. '''
data = data.sort_values(by=['latitude', 'longitude', 'year', 'month'])
inputs = data.drop([config["target"]], axis=1).values
outputs = data[[config["target"]]].values
outputs = np.where(outputs>700, 700, outputs)
lat_cnt = data['latitude'].unique().shape[0]
lon_cnt = data['longitude'].unique().shape[0]
feat_cnt = data.shape[-1]
inputs = inputs.reshape(lat_cnt, lon_cnt, -1, feat_cnt - 1)
outputs = outputs.reshape(lat_cnt, lon_cnt, -1, 1)
inputs = np.array([inputs[..., i - config['window']: i, :] for i in range(config['window'], inputs.shape[2] + 1)])
if config['add_history_target'] == True:
#inputs concat history target
input_add = np.array([outputs[..., i - config['window']: i, :] for i in range(config['window'], outputs.shape[2] + 1)])
input_add[:,:,:,(input_add.shape[3] - 1),:] = 0
input_add = np.nan_to_num(input_add, nan=0)
# print(input_add.shape)
inputs = np.concatenate((inputs, input_add), axis=4)
# if config['add_history_target'] == True:
# #inputs concat history target
# input_add = np.array([outputs[..., i - config['window']: i-1, :] for i in range(config['window'], outputs.shape[2] + 1)])
# new_shape = list(input_add.shape)
# new_shape[-2] += 1
# new_array = np.full(new_shape, np.nan)
# new_array[..., :input_add.shape[-2], :] = input_add
# input_add = new_array
# input_add = np.nan_to_num(input_add, nan=0)
# inputs = np.concatenate((inputs, input_add), axis=4)
outputs = np.array([outputs[..., i - 1, :] for i in range(config['window'], outputs.shape[2] + 1)])
print(inputs.shape, outputs.shape)
if mode == 'train':
batch_size = config['batch_size']
else:
batch_size = config['batch_size'] * 10
return inputs, outputs
# if mode=='train' and config['kfold']>1:
# kf = KFold(n_splits=config['kfold'], shuffle=shuffle)
# train_loaders , valid_loaders = [], []
# for train_idx, valid_idx in kf.split(inputs):
# train_dataset = OceanDataset(inputs[train_idx], outputs[train_idx], config, mode) # Construct dataset
# train_dataloader = DataLoader(
# train_dataset, batch_size,
# shuffle=train_shuffle, drop_last=False,
# num_workers=n_jobs
# )
# valid_dataset = OceanDataset(inputs[valid_idx], outputs[valid_idx], config, mode) # Construct dataset
# valid_dataloader = DataLoader(
# valid_dataset, batch_size,
# shuffle=valid_shuffle, drop_last=False,
# num_workers=n_jobs
# )
# train_loaders.append(train_dataloader)
# valid_loaders.append(valid_dataloader)
# return train_loaders,valid_loaders
# else:
# dataset = OceanDataset(inputs, outputs, config, mode) # Construct dataset
# dataloader = DataLoader(
# dataset, batch_size,
# shuffle=shuffle, drop_last=False,
# num_workers=n_jobs
# )
# return dataloader
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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