Datasets
Models
Diff of
/datasets/b_dataset.py
[000000]
..
[03464c]
Switch to unified view
| a | b/datasets/b_dataset.py | ||
|---|---|---|---|
| 1 | import os.path |
||
| 2 | from datasets import load_file |
||
| 3 | from datasets import get_survival_y_true |
||
| 4 | from datasets.basic_dataset import BasicDataset |
||
| 5 | from util import preprocess |
||
| 6 | import numpy as np |
||
| 7 | import pandas as pd |
||
| 8 | import torch |
||
| 9 | |||
| 10 | |||
| 11 | class BDataset(BasicDataset): |
||
| 12 | """ |
||
| 13 | A dataset class for methylation dataset. |
||
| 14 | DNA methylation data file should be prepared as '/path/to/data/B.tsv'. |
||
| 15 | |||
| 16 | For each omics file, each columns should be each sample and each row should be each molecular feature. |
||
| 17 | """ |
||
| 18 | |||
| 19 | def __init__(self, param): |
||
| 20 | """ |
||
| 21 | Initialize this dataset class. |
||
| 22 | """ |
||
| 23 | BasicDataset.__init__(self, param) |
||
| 24 | self.omics_dims = [] |
||
| 25 | self.omics_dims.append(None) # First dimension is for gene expression (A) |
||
| 26 | |||
| 27 | # Load data for B |
||
| 28 | B_df = load_file(param, 'B') |
||
| 29 | # Get the sample list |
||
| 30 | if param.use_sample_list: |
||
| 31 | sample_list_path = os.path.join(param.data_root, 'sample_list.tsv') # get the path of sample list |
||
| 32 | self.sample_list = np.loadtxt(sample_list_path, delimiter='\t', dtype='str') |
||
| 33 | else: |
||
| 34 | self.sample_list = B_df.columns |
||
| 35 | # Get the feature list for B |
||
| 36 | if param.use_feature_lists: |
||
| 37 | feature_list_B_path = os.path.join(param.data_root, 'feature_list_B.tsv') # get the path of feature list |
||
| 38 | feature_list_B = np.loadtxt(feature_list_B_path, delimiter='\t', dtype='<U32') |
||
| 39 | else: |
||
| 40 | feature_list_B = B_df.index |
||
| 41 | B_df = B_df.loc[feature_list_B, self.sample_list] |
||
| 42 | self.sample_num = B_df.shape[1] |
||
| 43 | if param.ch_separate: |
||
| 44 | B_df_list, self.B_dim = preprocess.separate_B(B_df) |
||
| 45 | self.B_tensor_all = [] |
||
| 46 | for i in range(0, 23): |
||
| 47 | B_array = B_df_list[i].values |
||
| 48 | if self.param.add_channel: |
||
| 49 | # Add one dimension for the channel |
||
| 50 | B_array = B_array[np.newaxis, :, :] |
||
| 51 | B_tensor_part = torch.Tensor(B_array) |
||
| 52 | self.B_tensor_all.append(B_tensor_part) |
||
| 53 | else: |
||
| 54 | self.B_dim = B_df.shape[0] |
||
| 55 | B_array = B_df.values |
||
| 56 | if self.param.add_channel: |
||
| 57 | # Add one dimension for the channel |
||
| 58 | B_array = B_array[np.newaxis, :, :] |
||
| 59 | self.B_tensor_all = torch.Tensor(B_array) |
||
| 60 | self.omics_dims.append(self.B_dim) |
||
| 61 | |||
| 62 | self.class_num = 0 |
||
| 63 | if param.downstream_task == 'classification': |
||
| 64 | # Load labels |
||
| 65 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
||
| 66 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 67 | self.labels_array = labels_df.iloc[:, -1].values |
||
| 68 | # Get the class number |
||
| 69 | self.class_num = len(labels_df.iloc[:, -1].unique()) |
||
| 70 | elif param.downstream_task == 'regression': |
||
| 71 | # Load target values |
||
| 72 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
||
| 73 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 74 | self.values_array = values_df.iloc[:, -1].astype(float).values |
||
| 75 | self.values_max = self.values_array.max() |
||
| 76 | self.values_min = self.values_array.min() |
||
| 77 | elif param.downstream_task == 'survival': |
||
| 78 | # Load survival data |
||
| 79 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
||
| 80 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 81 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
||
| 82 | self.survival_E_array = survival_df.iloc[:, -1].values |
||
| 83 | self.survival_T_max = self.survival_T_array.max() |
||
| 84 | self.survival_T_min = self.survival_T_array.min() |
||
| 85 | if param.survival_loss == 'MTLR': |
||
| 86 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
||
| 87 | if param.stratify_label: |
||
| 88 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
||
| 89 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 90 | self.labels_array = labels_df.iloc[:, -1].values |
||
| 91 | elif param.downstream_task == 'multitask': |
||
| 92 | # Load labels |
||
| 93 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
||
| 94 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 95 | self.labels_array = labels_df.iloc[:, -1].values |
||
| 96 | # Get the class number |
||
| 97 | self.class_num = len(labels_df.iloc[:, -1].unique()) |
||
| 98 | |||
| 99 | # Load target values |
||
| 100 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
||
| 101 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 102 | self.values_array = values_df.iloc[:, -1].astype(float).values |
||
| 103 | self.values_max = self.values_array.max() |
||
| 104 | self.values_min = self.values_array.min() |
||
| 105 | |||
| 106 | # Load survival data |
||
| 107 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
||
| 108 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 109 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
||
| 110 | self.survival_E_array = survival_df.iloc[:, -1].values |
||
| 111 | self.survival_T_max = self.survival_T_array.max() |
||
| 112 | self.survival_T_min = self.survival_T_array.min() |
||
| 113 | if param.survival_loss == 'MTLR': |
||
| 114 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
||
| 115 | elif param.downstream_task == 'alltask': |
||
| 116 | # Load labels |
||
| 117 | self.labels_array = [] |
||
| 118 | self.class_num = [] |
||
| 119 | for i in range(param.task_num-2): |
||
| 120 | labels_path = os.path.join(param.data_root, 'labels_'+str(i+1)+'.tsv') # get the path of the label |
||
| 121 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 122 | self.labels_array.append(labels_df.iloc[:, -1].values) |
||
| 123 | # Get the class number |
||
| 124 | self.class_num.append(len(labels_df.iloc[:, -1].unique())) |
||
| 125 | |||
| 126 | # Load target values |
||
| 127 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
||
| 128 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 129 | self.values_array = values_df.iloc[:, -1].astype(float).values |
||
| 130 | self.values_max = self.values_array.max() |
||
| 131 | self.values_min = self.values_array.min() |
||
| 132 | |||
| 133 | # Load survival data |
||
| 134 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
||
| 135 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
||
| 136 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
||
| 137 | self.survival_E_array = survival_df.iloc[:, -1].values |
||
| 138 | self.survival_T_max = self.survival_T_array.max() |
||
| 139 | self.survival_T_min = self.survival_T_array.min() |
||
| 140 | if param.survival_loss == 'MTLR': |
||
| 141 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
||
| 142 | |||
| 143 | def __getitem__(self, index): |
||
| 144 | """ |
||
| 145 | Return a data point and its metadata information. |
||
| 146 | |||
| 147 | Returns a dictionary that contains B_tensor, label and index |
||
| 148 | input_omics (list) -- a list of input omics tensor |
||
| 149 | label (int) -- label of the sample |
||
| 150 | index (int) -- the index of this data point |
||
| 151 | """ |
||
| 152 | # Get the tensor of B |
||
| 153 | if self.param.ch_separate: |
||
| 154 | B_tensor = [] |
||
| 155 | for i in range(0, 23): |
||
| 156 | if self.param.add_channel: |
||
| 157 | B_tensor_part = self.B_tensor_all[i][:, :, index] |
||
| 158 | else: |
||
| 159 | B_tensor_part = self.B_tensor_all[i][:, index] |
||
| 160 | B_tensor.append(B_tensor_part) |
||
| 161 | # Return a list of tensor |
||
| 162 | else: |
||
| 163 | if self.param.add_channel: |
||
| 164 | B_tensor = self.B_tensor_all[:, :, index] |
||
| 165 | else: |
||
| 166 | B_tensor = self.B_tensor_all[:, index] |
||
| 167 | # Return a tensor |
||
| 168 | |||
| 169 | # Get the tensor of A |
||
| 170 | A_tensor = 0 |
||
| 171 | |||
| 172 | # Get the tensor of C |
||
| 173 | C_tensor = 0 |
||
| 174 | |||
| 175 | if self.param.downstream_task == 'classification': |
||
| 176 | # Get label |
||
| 177 | label = self.labels_array[index] |
||
| 178 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'index': index} |
||
| 179 | elif self.param.downstream_task == 'regression': |
||
| 180 | # Get target value |
||
| 181 | value = self.values_array[index] |
||
| 182 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'value': value, 'index': index} |
||
| 183 | elif self.param.downstream_task == 'survival': |
||
| 184 | # Get survival T and E |
||
| 185 | survival_T = self.survival_T_array[index] |
||
| 186 | survival_E = self.survival_E_array[index] |
||
| 187 | y_true = self.y_true_tensor[index, :] |
||
| 188 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
||
| 189 | elif self.param.downstream_task == 'multitask': |
||
| 190 | # Get label |
||
| 191 | label = self.labels_array[index] |
||
| 192 | # Get target value |
||
| 193 | value = self.values_array[index] |
||
| 194 | # Get survival T and E |
||
| 195 | survival_T = self.survival_T_array[index] |
||
| 196 | survival_E = self.survival_E_array[index] |
||
| 197 | y_true = self.y_true_tensor[index, :] |
||
| 198 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'value': value, |
||
| 199 | 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
||
| 200 | elif self.param.downstream_task == 'alltask': |
||
| 201 | # Get label |
||
| 202 | label = [] |
||
| 203 | for i in range(self.param.task_num - 2): |
||
| 204 | label.append(self.labels_array[i][index]) |
||
| 205 | # Get target value |
||
| 206 | value = self.values_array[index] |
||
| 207 | # Get survival T and E |
||
| 208 | survival_T = self.survival_T_array[index] |
||
| 209 | survival_E = self.survival_E_array[index] |
||
| 210 | y_true = self.y_true_tensor[index, :] |
||
| 211 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'value': value, |
||
| 212 | 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
||
| 213 | else: |
||
| 214 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'index': index} |
||
| 215 | |||
| 216 | def __len__(self): |
||
| 217 | """ |
||
| 218 | Return the number of data points in the dataset. |
||
| 219 | """ |
||
| 220 | return self.sample_num |