Create a validation set
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@ -59,11 +59,15 @@ def create_dataset(data_file, label_file) -> None:
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Create a training and test dataset with a 70/30 split respectively
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"""
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data = read_fastq(data_file, label_file)
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train_test_split = 0.7
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with TFRecordWriter(TRAIN_DATASET) as train, TFRecordWriter(TEST_DATASET) as test:
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train_eval_test_split = [0.8, 0.1, 0.1]
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with TFRecordWriter(TRAIN_DATASET) as training, TFRecordWriter(
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TEST_DATASET
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) as test, TFRecordWriter(EVAL_DATASET) as evaluation:
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for element in data:
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if random() < train_test_split:
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train.write(element)
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if random() < train_eval_test_split[0]:
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training.write(element)
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elif random() < train_eval_test_split[0] + train_eval_test_split[1]:
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evaluation.write(element)
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else:
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test.write(element)
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@ -94,8 +98,11 @@ def read_dataset(filepath) -> TFRecordDataset:
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return batched_dataset
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def dataset_creation(data_file, label_file) -> Tuple[TFRecordDataset, TFRecordDataset]:
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def dataset_creation(
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data_file, label_file
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) -> Tuple[TFRecordDataset, TFRecordDataset, TFRecordDataset]:
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create_dataset(data_file, label_file)
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train_data = read_dataset(TRAIN_DATASET)
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eval_data = read_dataset(EVAL_DATASET)
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test_data = read_dataset(TEST_DATASET)
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return train_data, test_data
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return train_data, eval_data, test_data
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