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/datasets/c_dataset.py
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| a | b/datasets/c_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 | import numpy as np |
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| 6 | import pandas as pd |
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| 7 | import torch |
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| 8 | |||
| 9 | |||
| 10 | class CDataset(BasicDataset): |
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| 11 | """ |
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| 12 | A dataset class for miRNA expression dataset. |
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| 13 | File should be prepared as '/path/to/data/C.tsv'. |
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| 14 | For each omics file, each columns should be each sample and each row should be each molecular feature. |
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| 15 | """ |
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| 16 | |||
| 17 | def __init__(self, param): |
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| 18 | """ |
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| 19 | Initialize this dataset class. |
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| 20 | """ |
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| 21 | BasicDataset.__init__(self, param) |
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| 22 | self.omics_dims = [] |
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| 23 | self.omics_dims.append(None) # First dimension is for gene expression (A) |
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| 24 | self.omics_dims.append(None) # Second dimension is for DNA methylation (B) |
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| 25 | |||
| 26 | # Load data for C |
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| 27 | C_df = load_file(param, 'C') |
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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 = C_df.columns |
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| 34 | # Get the feature list for C |
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| 35 | if param.use_feature_lists: |
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| 36 | feature_list_C_path = os.path.join(param.data_root, 'feature_list_C.tsv') # get the path of feature list |
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| 37 | feature_list_C = np.loadtxt(feature_list_C_path, delimiter='\t', dtype='<U32') |
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| 38 | else: |
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| 39 | feature_list_C = C_df.index |
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| 40 | C_df = C_df.loc[feature_list_C, self.sample_list] |
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| 41 | self.C_dim = C_df.shape[0] |
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| 42 | self.sample_num = C_df.shape[1] |
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| 43 | C_array = C_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 | C_array = C_array[np.newaxis, :, :] |
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| 47 | self.C_tensor_all = torch.Tensor(C_array) |
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| 48 | self.omics_dims.append(self.C_dim) |
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| 49 | |||
| 50 | self.class_num = 0 |
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| 51 | if param.downstream_task == 'classification': |
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| 52 | # Load labels |
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| 53 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
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| 54 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 55 | self.labels_array = labels_df.iloc[:, -1].values |
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| 56 | # Get the class number |
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| 57 | self.class_num = len(labels_df.iloc[:, -1].unique()) |
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| 58 | elif param.downstream_task == 'regression': |
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| 59 | # Load target values |
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| 60 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
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| 61 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 62 | self.values_array = values_df.iloc[:, -1].astype(float).values |
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| 63 | self.values_max = self.values_array.max() |
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| 64 | self.values_min = self.values_array.min() |
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| 65 | elif param.downstream_task == 'survival': |
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| 66 | # Load survival data |
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| 67 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
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| 68 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 69 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
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| 70 | self.survival_E_array = survival_df.iloc[:, -1].values |
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| 71 | self.survival_T_max = self.survival_T_array.max() |
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| 72 | self.survival_T_min = self.survival_T_array.min() |
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| 73 | if param.survival_loss == 'MTLR': |
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| 74 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
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| 75 | if param.stratify_label: |
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| 76 | labels_path = os.path.join(param.data_root, 'labels.tsv') # get the path of the label |
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| 77 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 78 | self.labels_array = labels_df.iloc[:, -1].values |
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| 79 | elif param.downstream_task == 'multitask': |
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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 | |||
| 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 | |||
| 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 | elif param.downstream_task == 'alltask': |
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| 104 | # Load labels |
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| 105 | self.labels_array = [] |
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| 106 | self.class_num = [] |
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| 107 | for i in range(param.task_num-2): |
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| 108 | labels_path = os.path.join(param.data_root, 'labels_'+str(i+1)+'.tsv') # get the path of the label |
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| 109 | labels_df = pd.read_csv(labels_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 110 | self.labels_array.append(labels_df.iloc[:, -1].values) |
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| 111 | # Get the class number |
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| 112 | self.class_num.append(len(labels_df.iloc[:, -1].unique())) |
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| 113 | |||
| 114 | # Load target values |
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| 115 | values_path = os.path.join(param.data_root, 'values.tsv') # get the path of the target value |
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| 116 | values_df = pd.read_csv(values_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 117 | self.values_array = values_df.iloc[:, -1].astype(float).values |
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| 118 | self.values_max = self.values_array.max() |
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| 119 | self.values_min = self.values_array.min() |
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| 120 | |||
| 121 | # Load survival data |
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| 122 | survival_path = os.path.join(param.data_root, 'survival.tsv') # get the path of the survival data |
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| 123 | survival_df = pd.read_csv(survival_path, sep='\t', header=0, index_col=0).loc[self.sample_list, :] |
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| 124 | self.survival_T_array = survival_df.iloc[:, -2].astype(float).values |
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| 125 | self.survival_E_array = survival_df.iloc[:, -1].values |
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| 126 | self.survival_T_max = self.survival_T_array.max() |
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| 127 | self.survival_T_min = self.survival_T_array.min() |
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| 128 | if param.survival_loss == 'MTLR': |
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| 129 | self.y_true_tensor = get_survival_y_true(param, self.survival_T_array, self.survival_E_array) |
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| 130 | |||
| 131 | def __getitem__(self, index): |
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| 132 | """ |
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| 133 | Return a data point and its metadata information. |
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| 134 | |||
| 135 | Returns a dictionary that contains C_tensor, label and index |
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| 136 | input_omics (list) -- a list of input omics tensor |
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| 137 | label (int) -- label of the sample |
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| 138 | index (int) -- the index of this data point |
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| 139 | """ |
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| 140 | # Get the tensor of C |
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| 141 | if self.param.add_channel: |
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| 142 | C_tensor = self.C_tensor_all[:, :, index] |
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| 143 | else: |
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| 144 | C_tensor = self.C_tensor_all[:, index] |
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| 145 | |||
| 146 | # Get the tensor of A |
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| 147 | A_tensor = 0 |
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| 148 | |||
| 149 | # Get the tensor of B |
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| 150 | # Get the tensor of B |
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| 151 | if self.param.ch_separate: |
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| 152 | B_tensor = list(np.zeros(23)) |
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| 153 | else: |
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| 154 | B_tensor = 0 |
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| 155 | |||
| 156 | if self.param.downstream_task == 'classification': |
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| 157 | # Get label |
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| 158 | label = self.labels_array[index] |
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| 159 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'index': index} |
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| 160 | elif self.param.downstream_task == 'regression': |
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| 161 | # Get target value |
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| 162 | value = self.values_array[index] |
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| 163 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'value': value, 'index': index} |
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| 164 | elif self.param.downstream_task == 'survival': |
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| 165 | # Get survival T and E |
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| 166 | survival_T = self.survival_T_array[index] |
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| 167 | survival_E = self.survival_E_array[index] |
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| 168 | y_true = self.y_true_tensor[index, :] |
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| 169 | 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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| 170 | elif self.param.downstream_task == 'multitask': |
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| 171 | # Get label |
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| 172 | label = self.labels_array[index] |
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| 173 | # Get target value |
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| 174 | value = self.values_array[index] |
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| 175 | # Get survival T and E |
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| 176 | survival_T = self.survival_T_array[index] |
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| 177 | survival_E = self.survival_E_array[index] |
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| 178 | y_true = self.y_true_tensor[index, :] |
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| 179 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'value': value, |
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| 180 | 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
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| 181 | elif self.param.downstream_task == 'alltask': |
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| 182 | # Get label |
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| 183 | label = [] |
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| 184 | for i in range(self.param.task_num - 2): |
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| 185 | label.append(self.labels_array[i][index]) |
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| 186 | # Get target value |
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| 187 | value = self.values_array[index] |
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| 188 | # Get survival T and E |
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| 189 | survival_T = self.survival_T_array[index] |
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| 190 | survival_E = self.survival_E_array[index] |
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| 191 | y_true = self.y_true_tensor[index, :] |
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| 192 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'label': label, 'value': value, |
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| 193 | 'survival_T': survival_T, 'survival_E': survival_E, 'y_true': y_true, 'index': index} |
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| 194 | else: |
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| 195 | return {'input_omics': [A_tensor, B_tensor, C_tensor], 'index': index} |
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| 196 | |||
| 197 | def __len__(self): |
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| 198 | """ |
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| 199 | Return the number of data points in the dataset. |
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| 200 | """ |
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| 201 | return self.sample_num |
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| 202 |