forked from qoherent/modrec-workflow
80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
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import random
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from collections import defaultdict
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def split(dataset, train_frac=0.8, seed=42, label_key = "modulation"):
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"""
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Splits a dataset into smaller datasets based on the specified lengths.
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Parameters:
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dataset (list): The dataset to be split.
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lengths (list): A list of lengths for each split.
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Returns:
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list: A list of split datasets.
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"""
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rec_buckets = defaultdict(list)
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for data, md in dataset:
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rec_buckets[md["recid"]].append((data, md))
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rec_labels = {} #store labels for each recording
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for rec_id, group in rec_buckets.items():
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label = group[0][1][label_key]
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if isinstance(label, bytes): #if the label is a byte string
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label = label.decode("utf-8")
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rec_labels[rec_id] = label
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label_rec_ids = defaultdict(list) #group rec_ids by label
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for rec_id, label in rec_labels.items():
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label_rec_ids[label].append(rec_id)
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random.seed(seed)
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train_recs, val_recs = set(), set()
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for label, rec_ids in label_rec_ids.items():
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random.shuffle(rec_ids)
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split_idx = int(len(rec_ids) * train_frac)
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train_recs.update(rec_ids[:split_idx]) #pulls train_frac or rec_ids per label, guarantees all modulations are represented
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val_recs.update(rec_ids[split_idx:])
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# add the assigned recordings to the train and val datasets
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train_dataset, val_dataset = [], []
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for rec_id, group in rec_buckets.items():
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if rec_id in train_recs:
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train_dataset.extend(group)
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elif rec_id in val_recs:
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val_dataset.extend(group)
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return train_dataset, val_dataset
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def split_recording(recording_list, num_snippets):
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"""
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Splits a list of recordings into smaller chunks.
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Parameters:
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recording_list (list): List of recordings to be split
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Returns: yeah yeah
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list: List of split recordings
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"""
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snippet_list = []
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for data, md in recording_list:
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C, N = data.shape
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L = N // num_snippets
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for i in range(num_snippets):
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start = i * L
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end = (i + 1) * L
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snippet = data[:, start:end]
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# copy the metadata, adding a snippet index
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snippet_md = md.copy()
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snippet_md["snippet_idx"] = i
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snippet_list.append((snippet, snippet_md))
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return snippet_list
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