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