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/datasets/ab_dataset.py
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| a | b/datasets/ab_dataset.py | ||
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| 1 | import os.path |
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| 2 | from datasets import load_file |
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| 3 | from datasets import get_survival_y_true |
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| 4 | from datasets.basic_dataset import BasicDataset |
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| 5 | from util import preprocess |
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| 6 | import numpy as np |
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| 7 | import pandas as pd |
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| 8 | import torch |
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| 9 | |||
| 10 | |||
| 11 | class ABDataset(BasicDataset): |
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| 12 | """ |
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| 13 | A dataset class for multi-omics dataset. |
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| 14 | For gene expression data, file should be prepared as '/path/to/data/A.tsv'. |
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| 15 | For DNA methylation data, file should be prepared as '/path/to/data/B.tsv'. |
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| 16 | For each omics file, each columns should be each sample and each row should be each molecular feature. |
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| 17 | """ |
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| 18 | |||
| 19 | def __init__(self, param): |
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| 20 | """ |
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| 21 | Initialize this dataset class. |
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| 22 | """ |
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| 23 | BasicDataset.__init__(self, param) |
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| 24 | self.omics_dims = [] |
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| 25 | |||
| 26 | # Load data for A |
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| 27 | A_df = load_file(param, 'A') |
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| 28 | # Get the sample list |
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| 29 | if param.use_sample_list: |
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| 30 | sample_list_path = os.path.join(param.data_root, 'sample_list.tsv') # get the path of sample list |
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| 31 | self.sample_list = np.loadtxt(sample_list_path, delimiter='\t', dtype='<U32') |
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| 32 | else: |
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| 33 | self.sample_list = A_df.columns |
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| 34 | # Get the feature list for A |
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| 35 | if param.use_feature_lists: |
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| 36 | feature_list_A_path = os.path.join(param.data_root, 'feature_list_A.tsv') # get the path of feature list |
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| 37 | feature_list_A = np.loadtxt(feature_list_A_path, delimiter='\t', dtype='<U32') |
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| 38 | else: |
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| 39 | feature_list_A = A_df.index |
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| 40 | A_df = A_df.loc[feature_list_A, self.sample_list] |
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| 41 | self.A_dim = A_df.shape[0] |
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| 42 | self.sample_num = A_df.shape[1] |
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| 43 | A_array = A_df.values |
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| 44 | if self.param.add_channel: |
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| 45 | # Add one dimension for the channel |
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| 46 | A_array = A_array[np.newaxis, :, :] |
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| 47 | self.A_tensor_all = torch.Tensor(A_array) |
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| 48 | self.omics_dims.append(self.A_dim) |
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| 49 | |||
| 50 | # Load data for B |
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| 51 | B_df = load_file(param, 'B') |
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| 52 | # Get the feature list for B |
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| 53 | if param.use_feature_lists: |
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| 54 | feature_list_B_path = os.path.join(param.data_root, 'feature_list_B.tsv') # get the path of feature list |
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| 55 | feature_list_B = np.loadtxt(feature_list_B_path, delimiter='\t', dtype='<U32') |
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| 56 | else: |
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| 57 | feature_list_B = B_df.index |
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| 58 | B_df = B_df.loc[feature_list_B, self.sample_list] |
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| 59 | if param.ch_separate: |
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| 60 | B_df_list, self.B_dim = preprocess.separate_B(B_df) |
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| 61 | self.B_tensor_all = [] |
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| 62 | for i in range(0, 23): |
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| 63 | B_array = B_df_list[i].values |
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| 64 | if self.param.add_channel: |
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| 65 | # Add one dimension for the channel |
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| 66 | B_array = B_array[np.newaxis, :, :] |
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| 67 | B_tensor_part = torch.Tensor(B_array) |
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| 68 | self.B_tensor_all.append(B_tensor_part) |
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| 69 | else: |
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| 70 | self.B_dim = B_df.shape[0] |
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| 71 | B_array = B_df.values |
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| 72 | if self.param.add_channel: |
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| 73 | # Add one dimension for the channel |
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| 74 | B_array = B_array[np.newaxis, :, :] |
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| 75 | self.B_tensor_all = torch.Tensor(B_array) |
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| 76 | self.omics_dims.append(self.B_dim) |
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| 77 | |||
| 78 | self.class_num = 0 |
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| 79 | if param.downstream_task == 'classification': |
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| 80 | # Load labels |
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| 81 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
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| 82 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 83 | self.labels_array = labels_df.iloc[:, -1].values |
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| 84 | # Get the class number |
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| 85 | self.class_num = len(labels_df.iloc[:, -1].unique()) |
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| 86 | elif param.downstream_task == 'regression': |
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| 87 | # Load target values |
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| 88 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
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| 89 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 90 | self.values_array = values_df.iloc[:, -1].astype(float).values |
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| 91 | self.values_max = self.values_array.max() |
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| 92 | self.values_min = self.values_array.min() |
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| 93 | elif param.downstream_task == 'survival': |
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| 94 | # Load survival data |
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| 95 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
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| 96 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 97 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
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| 98 | self.survival_E_array = survival_df.iloc[:, -1].values |
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| 99 | self.survival_T_max = self.survival_T_array.max() |
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| 100 | self.survival_T_min = self.survival_T_array.min() |
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| 101 | if param.survival_loss == 'MTLR': |
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| 102 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
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| 103 | if param.stratify_label: |
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| 104 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
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| 105 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 106 | self.labels_array = labels_df.iloc[:, -1].values |
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| 107 | elif param.downstream_task == 'multitask': |
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| 108 | # Load labels |
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| 109 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
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| 110 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 111 | self.labels_array = labels_df.iloc[:, -1].values |
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| 112 | # Get the class number |
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| 113 | self.class_num = len(labels_df.iloc[:, -1].unique()) |
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| 114 | |||
| 115 | # Load target values |
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| 116 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
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| 117 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 118 | self.values_array = values_df.iloc[:, -1].astype(float).values |
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| 119 | self.values_max = self.values_array.max() |
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| 120 | self.values_min = self.values_array.min() |
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| 121 | |||
| 122 | # Load survival data |
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| 123 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
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| 124 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 125 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
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| 126 | self.survival_E_array = survival_df.iloc[:, -1].values |
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| 127 | self.survival_T_max = self.survival_T_array.max() |
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| 128 | self.survival_T_min = self.survival_T_array.min() |
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| 129 | if param.survival_loss == 'MTLR': |
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| 130 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
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| 131 | elif param.downstream_task == 'alltask': |
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| 132 | # Load labels |
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| 133 | self.labels_array = [] |
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| 134 | self.class_num = [] |
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| 135 | for i in range(param.task_num-2): |
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| 136 | labels_path = os.path.join(param.data_root, 'labels_'+str(i+1)+'.tsv') # get the path of the label |
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| 137 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 138 | self.labels_array.append(labels_df.iloc[:, -1].values) |
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| 139 | # Get the class number |
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| 140 | self.class_num.append(len(labels_df.iloc[:, -1].unique())) |
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| 141 | |||
| 142 | # Load target values |
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| 143 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
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| 144 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 145 | self.values_array = values_df.iloc[:, -1].astype(float).values |
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| 146 | self.values_max = self.values_array.max() |
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| 147 | self.values_min = self.values_array.min() |
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| 148 | |||
| 149 | # Load survival data |
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| 150 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
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| 151 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 152 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
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| 153 | self.survival_E_array = survival_df.iloc[:, -1].values |
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| 154 | self.survival_T_max = self.survival_T_array.max() |
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| 155 | self.survival_T_min = self.survival_T_array.min() |
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| 156 | if param.survival_loss == 'MTLR': |
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| 157 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
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| 158 | |||
| 159 | def __getitem__(self, index): |
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| 160 | """ |
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| 161 | Return a data point and its metadata information. |
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| 162 | |||
| 163 | Returns a dictionary that contains A_tensor, B_tensor, C_tensor, label and index |
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| 164 | input_omics (list) -- a list of input omics tensor |
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| 165 | label (int) -- label of the sample |
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| 166 | index (int) -- the index of this data point |
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| 167 | """ |
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| 168 | # Get the tensor of A |
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| 169 | if self.param.add_channel: |
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| 170 | A_tensor = self.A_tensor_all[:, :, index] |
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| 171 | else: |
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| 172 | A_tensor = self.A_tensor_all[:, index] |
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| 173 | |||
| 174 | # Get the tensor of B |
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| 175 | if self.param.ch_separate: |
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| 176 | B_tensor = [] |
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| 177 | for i in range(0, 23): |
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| 178 | if self.param.add_channel: |
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| 179 | B_tensor_part = self.B_tensor_all[i][:, :, index] |
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| 180 | else: |
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| 181 | B_tensor_part = self.B_tensor_all[i][:, index] |
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| 182 | B_tensor.append(B_tensor_part) |
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| 183 | # Return a list of tensor |
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| 184 | else: |
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| 185 | if self.param.add_channel: |
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| 186 | B_tensor = self.B_tensor_all[:, :, index] |
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| 187 | else: |
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| 188 | B_tensor = self.B_tensor_all[:, index] |
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| 189 | # Return a tensor |
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| 190 | |||
| 191 | # Get the tensor of C |
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| 192 | C_tensor = 0 |
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| 193 | |||
| 194 | if self.param.downstream_task == 'classification': |
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| 195 | # Get label |
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| 196 | label = self.labels_array[index] |
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| 197 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'index': index} |
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| 198 | elif self.param.downstream_task == 'regression': |
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| 199 | # Get target value |
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| 200 | value = self.values_array[index] |
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| 201 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'value': value, 'index': index} |
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| 202 | elif self.param.downstream_task == 'survival': |
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| 203 | # Get survival T and E |
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| 204 | survival_T = self.survival_T_array[index] |
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| 205 | survival_E = self.survival_E_array[index] |
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| 206 | y_true = self.y_true_tensor[index, :] |
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| 207 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
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| 208 | elif self.param.downstream_task == 'multitask': |
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| 209 | # Get label |
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| 210 | label = self.labels_array[index] |
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| 211 | # Get target value |
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| 212 | value = self.values_array[index] |
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| 213 | # Get survival T and E |
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| 214 | survival_T = self.survival_T_array[index] |
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| 215 | survival_E = self.survival_E_array[index] |
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| 216 | y_true = self.y_true_tensor[index, :] |
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| 217 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'value': value, |
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| 218 | 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
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| 219 | elif self.param.downstream_task == 'alltask': |
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| 220 | # Get label |
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| 221 | label = [] |
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| 222 | for i in range(self.param.task_num - 2): |
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| 223 | label.append(self.labels_array[i][index]) |
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| 224 | # Get target value |
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| 225 | value = self.values_array[index] |
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| 226 | # Get survival T and E |
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| 227 | survival_T = self.survival_T_array[index] |
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| 228 | survival_E = self.survival_E_array[index] |
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| 229 | y_true = self.y_true_tensor[index, :] |
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| 230 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'value': value, |
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| 231 | 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
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| 232 | else: |
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| 233 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'index': index} |
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| 234 | |||
| 235 | def __len__(self): |
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| 236 | """ |
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| 237 | Return the number of data points in the dataset. |
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| 238 | """ |
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| 239 | return self.sample_num |
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| 240 |