add Dataset

Signed-off-by: Zhiyuan Chen <[email protected]>

multimolecule/__init__.py

@@ -1,4 +1,5 @@
-from . import models, tokenisers
+from . import data, models, tokenisers
+from .data import Dataset, PandasDataset
 from .downstream.crispr_off_target import (
     RnaBertForCrisprOffTarget,
     RnaFmForCrisprOffTarget,
@@ -54,9 +55,12 @@
 from .tokenisers import RnaTokenizer
 
 __all__ = [
-    "models",
+    "data",
+    "Dataset",
+    "PandasDataset",
     "tokenisers",
     "RnaTokenizer",
+    "models",
     "RnaBertConfig",
     "RnaBertModel",
     "RnaBertForMaskedLM",

multimolecule/data/__init__.py

@@ -0,0 +1,3 @@
+from .dataset import Dataset, PandasDataset
+
+__all__ = ["Dataset", "PandasDataset"]

multimolecule/data/dataset.py

@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+from collections import OrderedDict
+from collections.abc import Sequence
+
+import danling as dl
+import datasets
+import torch
+from chanfig import FlatDict
+from pandas import DataFrame
+from tokenizers import Tokenizer
+from transformers import AutoTokenizer, PreTrainedTokenizerBase
+from datasets.formatting import query_table
+
+
+class Dataset(datasets.Dataset):
+
+    data_cols: Sequence
+    feature_cols: Sequence
+    label_cols: Sequence
+    tokenizer: PreTrainedTokenizerBase | Tokenizer
+    sequence_cols: Sequence
+    rename_sequence: bool
+    preprocess: bool
+
+    def post(
+        self,
+        tokenizer: Tokenizer | PreTrainedTokenizerBase | None = None,
+        pretrained: str | None = None,
+        feature_cols: Sequence | None = None,
+        label_cols: Sequence | None = None,
+        preprocess: bool = True,
+        rename_sequence: bool | None = None,
+    ):
+        self.sequence_cols = [k for k, v in self.features.items() if v.dtype == "string"]
+
+        data_cols = list(self._info.features.keys())
+        if label_cols is None:
+            label_cols = [i for i in data_cols if i not in feature_cols] if feature_cols is not None else ["label"]
+        if feature_cols is None:
+            feature_cols = [i for i in data_cols if i not in label_cols]
+        missing_feature_cols = set(feature_cols).difference(data_cols)
+        if missing_feature_cols:
+            raise ValueError(f"{missing_feature_cols} are specified in feature_cols, but not found in dataset.")
+        missing_label_cols = set(label_cols).difference(data_cols)
+        if missing_label_cols:
+            raise ValueError(f"{missing_label_cols} are specified in label_cols, but not found in dataset.")
+        self.feature_cols = list(feature_cols)
+        self.label_cols = list(label_cols)
+        self.data_cols = self.feature_cols + self.label_cols
+
+        if tokenizer is None:
+            tokenizer = AutoTokenizer.from_pretrained(pretrained)
+        if tokenizer is None:  # Actually means both tokenizer and pretrained is None
+            raise ValueError("Either tokenizer or pretrained must be specified")
+        self.tokenizer = tokenizer
+
+        self.preprocess = preprocess
+        if self.preprocess:
+            self.update(self.map(self.tokenization))
+            self.set_transform(self.torch_transform)
+        else:
+            self.set_transform(self.tokenize_transform)
+
+        if rename_sequence is None:
+            rename_sequence = len(self.sequence_cols) == 1
+        self.rename_sequence = rename_sequence
+        if self.rename_sequence:
+            sequence_col = self.sequence_cols[0]
+            self.update(self.rename_column(sequence_col, "input_ids"))
+            self.sequence_cols = ("input_ids",)
+            self.feature_cols = ["input_ids" if i == sequence_col else i for i in self.feature_cols]
+            self.label_cols = ["input_ids" if i == sequence_col else i for i in self.label_cols]
+            self.data_cols = ["input_ids" if i == sequence_col else i for i in self.data_cols]
+
+    def update(self, dataset: datasets.Dataset):
+        # pylint: disable=W0212
+        # Why datasets won't support in-place changes?
+        # It's just impossible to extend.
+        self._format_columns = dataset._format_columns
+        self._data = dataset._data
+        self._info = dataset._info
+        self._fingerprint = dataset._fingerprint
+
+    def tokenization(self, data):
+        return {col: self.tokenizer(data[col], return_attention_mask=False)["input_ids"] for col in self.sequence_cols}
+
+    def torch_transform(self, batch):
+        return {k: dl.PNTensor(v) if k in self.sequence_cols else torch.tensor(v) for k, v in batch.items()}
+
+    def tokenize_transform(self, batch):
+        return {
+            k: (
+                dl.PNTensor(self.tokenizer(v, return_attention_mask=False)["input_ids"])
+                if k in self.sequence_cols
+                else torch.tensor(v)
+            )
+            for k, v in batch.items()
+        }
+
+    def __getitem__(self, key: int | slice | str | Sequence[int], **kwargs) -> OrderedDict:
+        batch = self._getitem(key, **kwargs)
+        input = FlatDict({col: batch[col] for col in self.feature_cols})
+        target = FlatDict({col: batch[col] for col in self.label_cols})
+        return OrderedDict(input=input, target=target)
+
+
+class PandasDataset(Dataset):
+
+    def __init__(
+        self,
+        dataframe: DataFrame | str,
+        split: str,
+        tokenizer: Tokenizer | PreTrainedTokenizerBase | None = None,
+        pretrained: str | None = None,
+        feature_cols: Sequence | None = None,
+        label_cols: Sequence | None = None,
+        preprocess: bool = True,
+        rename_sequence: bool | None = None,
+    ):
+        if isinstance(dataframe, str):
+            dataframe = dl.load(dataframe)
+        if isinstance(dataframe, dict):
+            dataframe = DataFrame.from_dict(dataframe)
+        dataframe = dataframe.loc[:, ~dataframe.columns.str.contains("^Unnamed")]
+        table = datasets.table.InMemoryTable.from_pandas(dataframe, preserve_index=False)
+        super().__init__(table, split=split)
+        self.post(
+            tokenizer=tokenizer,
+            pretrained=pretrained,
+            feature_cols=feature_cols,
+            label_cols=label_cols,
+            preprocess=preprocess,
+            rename_sequence=rename_sequence,
+        )

pyproject.toml

@@ -46,6 +46,8 @@ dynamic = [
 dependencies = [
   "chanfig>=0.0.99",
   "danling",
+  "datasets",
+  "tokenizers",
   "transformers",
 ]
 [project.urls]

tests/data/datasets/rna/5utr.csv

@@ -0,0 +1,68 @@
+,seq,label
+0,ACCAACATGTAATTTCCACTCTTGA,-1.7812239923226838
+1,TGGTAAAATCTAGGGTTTTTTATAA,-0.275642799200671
+2,CAAAAAGTAGACGCAACATGAAAAA,-1.1482258696972798
+3,TGGCCTCGTGGATAGGACATTTGGA,-1.163470221256106
+4,TAATCGGTTCTAAATACGATTAGTG,0.629766441913879
+5,TAAAGAAGAGGTTGATGAGAAACCG,-0.0566107803048019
+6,AAGCGGTGAATAACACACAGTAAAG,0.4379278257655004
+7,ATCTCTCTAGTACAGATTGTCAATA,0.6259435716867096
+8,AGACAGCTAAAACCCTACAAAATAA,0.4289960198091346
+9,CCTTCGACGACCCACGTCCGCCTTA,0.0669630515711629
+10,CGTTGATCATGGATACTTTTTTACA,-1.4310155058894878
+11,GTACGCAAACCATCTCTCGATTTCT,0.3852448242284625
+12,GTTACCCCCTACTCCAGCTCATACT,0.1064178675873167
+13,TCCAATCTTTTGCACCACCCCTAGG,0.1675831224155592
+14,CTCCCTCAACAGGTGCCTCACGCTG,0.4482936797119086
+15,AGTAATGAGTTTCGGCATTTCAAAG,0.4487089779004714
+16,AGGATTGTGTCGCCAGTTCCACTGA,0.2266517419179321
+17,TAATATCATATAGTTCTTCTCCCCT,0.0910128870657181
+18,TAGAATCGGAAGGAATAGGATTCTA,0.6830431344740635
+19,GATGCTTGCACTCGAGGTCCGTGCA,0.7779586432740309
+20,GACACCACGTAAAATCCTAATCAAA,0.7227716269767893
+21,TCTATGACTCGTTCGCGTAGAATCA,-0.9032312196091278
+22,CAAAATGATAAGATGGACCAAAGAT,0.0447788302836263
+23,TGCATGATCTGTAGCATTTGCTGCT,-0.2827116234508076
+24,GCATGACCAGCCTGTTTAGATAGAA,-0.8848997916225679
+25,AGAAAGATAACAAACCACCCGTATG,0.5311009416510439
+26,GACCCCTTTACGCAACCTATTGAAC,0.7381938795002485
+27,GCCCCTACACTCTGTTTTTTGATCC,0.4626627635505125
+28,GGATAAATAAATCTGAGATCAGAAA,0.5764032765766933
+29,CCCTGTTGCCAGCCGCATAATCATC,0.5072462083719866
+30,GCAGCACGCTTACAGTCCCTCAGAC,0.5885318168197583
+31,CTTTTTCCTTACTCGTGATACTATC,0.3549980256557335
+32,GTAAACCCAGATCTAGTTTGACTGT,0.4340458948389251
+33,CACGCTGCACACCGAACAGCCCAAA,-0.0060640299062117
+34,ACTCCGACACCATCTTCATTACAAT,0.40927053992064
+35,TACATGGAACTGTCCCTTCTTACCG,-0.8422048835483932
+36,GACCCTCCTATTATCAACCAAGATA,0.2085787716855296
+37,AGAGTGAGAGCGCGACAAATCACTG,0.677525749419415
+38,CCGATTGGCGCCCTTTGGCCGGGAG,0.0662045936850974
+39,GAGATGAGAAGTCGTGCGAAATAAC,-1.5323635165013456
+40,GTCCTCGCGACAACTGTCCCAAACC,0.2904891214718897
+41,TTCTGATCGGTGTTCCTCCGTTCTG,0.4886688832278358
+42,TCTAGTCGTTTCTAGCATAGACTATA,0.6682424782790564
+43,GCAATGCATCCATTCCAATGCCTACT,-1.1913139865591946
+44,ACCTTGCCGCATCCCACTTGCCTGCA,0.458991405155542
+45,CAAACTGGGCCCATTTCTATACCAAT,0.1781652824101883
+46,ACGAGAGTAACAGATCCAACCTAAA,0.6019488593566376
+47,CCTACGCGGGATGCTCTTTTTTATAG,-1.1687525556467426
+48,GATCCAGGAGACAGAAACCATCTACC,0.4738692979644047
+49,TCGCAAAGAAGAACCTATTTTAAGA,0.7018982636372705
+50,TATAATTACGCTTTTCCGTGTATGG,-0.3720488657064282
+51,TCAATTACAGCTCGACTTCCATGATC,0.2754572607942968
+52,AAGCCGTTCTTTAAATCCACACATTT,0.2832481855967742
+53,AGTCCATCCTCGCGGCCTCACACCA,-0.1678433893986053
+54,AGTCCCGTCCTACACGCTCGGTCCG,0.3135193265556327
+55,CCCCATATCCGATTATCTGCTGGAC,0.5673113165112577
+56,CGTAGTGGCGCAGGACCGTCAATTA,0.3736517875688682
+57,CTCTGCTATGCCCCACCACTCAACA,0.5126163959293235
+58,ATCCACCAATCCCTACATTCATCTTC,0.5112259267226038
+59,GAGAGTGTCGCCGAAGCACAAGCCGA,0.4693891586433297
+60,CCTGTCGATCTAGGTCCTATTGTCCG,0.6496399244427643
+61,ATTTCTAACTTCTTCTGGCAACGACA,0.5061690522661538
+62,ATATACGGCAACACGCCCGAACCAGA,0.2119265981391584
+63,CCTCGTTAATCCTTCCCTTGTCTCCC,0.1640642263497583
+64,TCCCCGCCACGCCCGGTATCCGACTA,-0.0315210929562356
+65,GAAACTCGTGTTTATTCTCGTCGAT,0.7040646602119047
+66,AAGAAAACATACAAGTCTGTTCACT,0.6293633976161706

tests/data/datasets/rna/modification.json

@@ -0,0 +1 @@
+{"sequence":{"0":"TTGCCACACTGCTGGACGCCTGCAAGGCCAAGGGTACGGAGGTCATCATCATCACCACCGATACCTCGCCCTCAGGCACCAAGAAGACCCGGCAGTATCTC","1":"TTTGAAAAAATATTAGCAATGTGAGGACACTTAAGCAGTTTTGTCAATTCAGCTGAATCCAGCCTCATAGCAAAATCTGGTCTTAAATTCCCTCATCGTGC","2":"AGAAACATTCAACCTCCCTTCTTTTTATTCCAGTTGTCCTTTTCTCTGACACTTGCATCAATTTTCTGATTGCCTAGGCTCTTAATATTGCTTTCTGTTCA","3":"TTAGTTTTACTATGGAATCATAATAACCCACATAGAAGACTGATATTAAGAGCACAGAAGAAATAGTCCCAATGTTTATGTCATTTAATTTGAAAAATTTC","4":"CAACAGAAGTTTCTCATCTATAATCAGTAGCACTAAACTCTTGGTTTGAAAAATATTTAGTATGGGTAATACTTGGAGTATCAGTTTTCATTAAAATGTAC","5":"AATGTGTTTGTGTGTGTCTCTCACACACACACATAACATGTACATACCTGAAACTCATACTGCAATTGCAACACATCTTAAGTTTTTCCTTTTAAACATAC","6":"AAGTAGAAGACAACAGTACTCTTTTTTTTTTGAAATGGAGTCTCACTCTCACCCAGGCTGGAGTGCAATGGTGTGATCTCGGCCCACTGCACTCCAGCCTG","7":"GCCACCACACGTGGCCACAGTTTGGGCTTTTGAAAAAAGTTAGGTGGAGGAAGAGAGGTATGAGTACTCTAGTTTTCACTGCAGTATCCCATTTGTGTGTG","8":"AGGCTGTTTTAGCTTAAGTAAAATTTAAAAATTAGTTCCTTAGTCACATTAGCCACATTTAATGTGTTCTATAGCCACGTGTGACTGGTGGCTAACATATT","9":"GCAAGTGGTGTTTGGTTACATGAATAAGTTCTTTAGTGGGGATTTCTGAAATTTTGGTGCACCCATCACCTGAGCAGTGTACACTGTATCCAATGTGTAGT","10":"TTCTCAGGATATGTTATAGGATTCTTCTGACCACTAGAGTAGAGTGAACGATATGTTTTAATGTTCAGAAGTCACTATGGAGTAAACCAAATATATATAGG","11":"TTTTCCAGGATTTCATGAAACAAAGAGTTAAGAACTACAGTAGTGGAGCAATATTCATGGTGCTTTTTCTTTTTCTTTTGAAATAATTAAAAACTTACAGA","12":"GTATTGTCGTCTCACTCTATTATCAGCCTACCTCCGGTGGCCCTTGGGGCATGTGGCTGGGCCCAGGGTGATTCATCTAGAGCCAGCTCAGGTGGCAGTGA","13":"GGTTTTTTTTTTTTTTTTTTAGTCCATCCATTCTTTGATTTAATTTGGCAAACCCACATTAGATAATTTAGCAGAAGAGGAATTATATCTTCATCCTATTA","14":"AAGAAACCTGAACCAAGGCCTTGGGTATCAGATTGGCTGGATAAGGAGGGATGAGCACAGAAGGAAGGACAAAGATAATACCTTTTTCAAGATGAGCCTGT","15":"CTCATTTTGTAAGGAGACACTTAGATGCATTTCTGAAAAAAACAAAACAAAACAAAACAAAACAAAAAACACTTTGGGCTTTCTCTGTATTCTTCAAGCAT","16":"GTAAGTGAGATTACTTTATTTATTTCTTTTTCAGATTGTTCACTGTTGGCATATAGAAATGCTACTAATTGTTGTATGTTGATTTTGTATCCTGAAACTTT","17":"CATGCCTGTAATCCCACCTACTCAGGAGGCTGACGCAGGAGAATTGCTTGAATCCGGGAGGTGGAGGTTGCAGTGAGCCAAGATCACGCCACTGCACTCCA","18":"GGGTCCAGCCCAGGCTGTTTGGTCCCAGAGCCTGTGCTCTTGTCCATTATACTGGTGGTATTGCCCCTGGCATTGACAAAGTGGGAAAAGATGACTAACCT","19":"TGGCTCACACCTGTAATCCCTGCACTTTGGGAGGCCAAGGTGAGCAGATCACTTGAGGTCAGGAGTCTTGAGACCAGCCTGGCCAACATGGTGAAGCCCTA","20":"ATATGAATGATTTGTCATTTATGTCTAATCACTAAGTAAAAATATCAATTATGATTACTTTTTAAGTTTTATTGATGCATAATTATACATATTTATGGGGT","21":"ACATCAAAAAGTTTGAAAGAGCACAAATAGACAACCAAGGGTCACACGTCATGGAACTGGAGAAACAAGAACAATAGAAACCCAAACCTAGCAGAAGAAAA","22":"CTATCAGAAATAATGAAAAAACTCACCTTTGGGATTTTCATTAGTTTGGCAATCACTTCTCCTTTTGAAAGATTGGTGGACTGTACATTTTATTATTATTA","23":"TGATCTTATTTGTTTCTGTGTCTTGAAATAGTTTGCTGTTTTGTCATCTTAGAAATTGATTCATTATTAACTCATTTATTCTCAACTATGCTAAAAAAAAG","24":"AAACCACAAAGATGGGGAGAAACCAGAGCAGAAAAGCTGAAAAGTTCAAAAAACCAGAGCACCTCTTCTCCTCCAAAGGATTGCAGTTCCTCACTGCAAAG","25":"CTACACAAGGTATTTCACAATATCCTTAGGAATTACTGAGTTTTAGAGTGACAGAATAATTACCAATTATTCTGATAGTAAATTTGTAGGTACATTATAAT","26":"GCTACCTCTACTTTTAACATATTTTAGGCATTAGGACTTGCTTAGCCTTTAATACACAGGAATATTAACTAAAATGCACATATAAAACAATTGGTTAGACA","27":"CCTGGATCTAAAAGTGTTTTTATTTTTTGTGCCCACATCTGTAGTCATGGATTTGATGTATATATTTAATAACATTCAGTGATTTATTTTTCGGTTCACCT","28":"TCTGAAGTCATAGTCCCTTGGTTTTCCCTGACCTGCCTGCTACTGCGCCCACTTGCAGCAGCACCTCCGTTGCCCAGTGAAGCATGCTGCCCTGGTCTTAC","29":"CTGGGGCGGGCGGGTCAGTTGAGGCCAGGAGTTCGAGACCAGCCTGGCCAACGTGGCAAAACCCTGTCTCTACTAAAAATACAAAAAAGTTAGCTAGGCGT","30":"AATATTGCATGGGCCATACTTATATTTTTAAAATATTCATTGTTTATCAGAATTCAAATTTAACTGGGCATCCTGTATTTTTATTAGCTAAATCTGGCAAC","31":"GACTAGCTGCAGAAAGTGACATTTACACTGGGACAGGAGTCAAAGAGTATATTGATGCAAAGGAAAGACCATGAATTAGACCTGAGTTCAAATCCTAGCCG","32":"AGAAAAAGACAGAGGTTTATAGAAGTTTTTTCCACAAAATTTATTTGTGCATTAATCGATAGGCAACATAGTGTAAAACATAGCTAGCTGAATATTCAGAA","33":"TGCCACTATTGGGGTAACCCACCCCCAATATTACAACATAGGTTCTTTCTATTTTCCATAAGTGTTGGCTGGCTGAGAAATAAAGAGAAAGAGTACAAAGA","34":"TGGAAGGAAGAATTGCTTTTCTGAGGTCAATGCTCAGCTTGGCTGTTGGCAAGTCAACCTTTAGGAATCTGTGTATTCAGGGTATAGCAGTGGAAGTATAG","35":"AAAATCAGCAGCTAGTATTTGCAAATGGTGTTTGTATTTACTCTTGAAATACATGGTTTTGTGCTGGAGATTTGGAGTAAGGAAACTTAGGCACTATAGTC","36":"TCCACTTGCTGCATTATTTTTTTCTTTCTTTTTTTTTGCTGATTATTTTTATATGAATGTTAAATGATAAAGTCTTCTACATCATATCCCATTTAAGCTGC","37":"TGTTTTTACATTGAAAGTAGACAAATAGTTTTGTCATCTGTTTCTCATCCATTTCTAATATTTAAATATAATAAAGTCTAATTGAATACAAAAACAAACAA","38":"AAAGGATGACGAAGTGTAGAGAAGAGGCCAGCCATAGGAAAAGGGGAGTCACTTATGGGAAGGTGACTAGGAAATGTGTGATATACAGGGGTTGTTAGTAA","39":"GGGCCGTCCTGAACACTGCCACCTCTGAGCGTTGGCATCCATCTGCTAGGATTAGCATTGGAGCTTTTTTTGAAGGTATTTTGAAGTCTAATGGGAGAGGA","40":"TCCCCAGGCTGGAGTGCAATGGCACAATCACAGCATACCTCCCAGGCTCAAGCAATCCTCCCACCTCAGCCTTTTGAGTAGCTGGGACCAGAAGCACGTGC","41":"ATTATGGCCCAGCCTATACCCAGAAGAGAGGACTTAACTTGTGCTCCATGAACCACTGTGTCTGGGACACTGAGTAACCTAAGAATTTTCTTTGATATGAC","42":"TCAGCCTCCCGAGTAGGTAGGATTACAGGCATGCGCCACCATGACCGATTAATTTTGTATTTTTGGTAGAGACGGGGTTTCACCATGTTGGTCAGGCTGGT","43":"AAATTCATTTTTTCAATCATTTAAGGAACTTAGATATAAAATACACCTTTAATTCACCTTTGGAAATTTTTTACAAAGTGTTTTATTTGCAAATGACAGTG","44":"ATTAGTTATTTCAGTGTTTATTTCATTTGATGAAGAAACGTTTGCATATGAATGTTGGGAATTCTAGCAGGTCCTGCCTCAATGTGAAGAGGCATTTTTTT","45":"CAGGTGCCTGCCACCATGCCTGGCTTATTTTTGTATTTTTAGTAGAGACAAGGTTTCACCAGGTTGGCCACTCCTGGTCTTGAACTCCTGACCTCAGGTGA","46":"TTTTTTTTTTTTTTTTTTTTACTGTGTCCCAGGCTTAAGAAAAAAGTGATACATGATGTGGGATTAAAATCAAGAACATCATTGAACTTCACCTTCCCTCC","47":"CGGGAGGCACGGGCCCTTCGGGGATGACGTCACGGGCGGGGGCCCCGGACACGCGAGCCTTGCGCCCCACAGACGGCGGCGCAGCCCGCCGCCCTTTTCGA","48":"TGAGGCTTAAGTGATCCTCCCACCTTAGCCTCCTAAGTAGCTGGGAGTACAAATGCACACCACCACACCTGGCTAATTTTTGTATTTTTTGTTTTGCCATG","49":"ACTCATAGCTCTATGTCTCTTATAGTTCTTAGCACAATATCTTGGCCTAGATGAAGTACATAATAATTATATGTAGGGTTGTGGAAAGCAGTGCTGGCTTT","50":"GGCTCCTTCGGAGGCAGAATATGTCAACTCGTTGGCTTCTCACAAAATCAAGTGAGTCAGAAACCTGAATGGGGTTTCGGCTGGTCTCACCTAATTAACTT","51":"TATCTACCACCTGGATTCTACAACTGACATTTTATTATACCTAGTTTTTTACATGTCTGTCCATCTGTCTCATCCATAGATCCATTTTATTTCTTTATACA","52":"CATGTATGTATACTTAACTAAGTTAATAAAAACTGTCCTATTTCTCCTGGACATTAGAGAGATCTCAGAACTCTTTAACTCCGTGTACCCACCTCCTGACT","53":"GGAGCTGGTTCAGGAGATCACACAACATTTATTCTTCTTACAGGTACATCAGTCAAGGCTACCCCCCAGTTCTGAGAGAACTTGCCCAGGAGTGGTTGCAG","54":"TTCCTGGTTGGTTGAATCACTGGATGCGGTACCCACGGATGCAGAGAGTGACTGTACAGAAAAAAAGCATCTATTGCCTTTCCAGGCCAAGCTTTCTGTCT","55":"ACATTTTAGAAAATAAAATGCACCGAACAAACATGGGGTGTTCCTACCGCAGCATGGGAAAGGCGAGGCGCCATCCCACCAAGGCGGGTGTGGTTTTGAGC","56":"GAACGAAAAGAGGAAGTAGTGAGTGAAAAGGAAAGAAGAAAACATTAAGAAGTAGAGGAAAAAGAATTAAGTCGATTAGATGCAATGAGGGAAGAGGAAAA","57":"GAGAAACAGTGACAAATTCTGAGGGGAGCCTACAGTGTATAGTGTTGTGTATAGTGTGTATAGTATATAGTGGTTGTGTATAGTGGCCTCTGCCTTTTACC","58":"CCTTGCCAATCCCCATGAAAATGTTCAGTTATGTCAAAAGCAAGGCAAAAACAGTCTCTTGGCTATACAAGGGTAGCTGTTTTATTTGACTAAAATTTAGC","59":"ATTGTAGTGCAAAGCAGCCACAGACAAAATTTAAATGAATGAACCTGGCCATATTCCAATAAAATGAATTTGAATTTCAAATAATTTTTATGTGTCATAAA","60":"TGAGAAGAAAGAAAGAAAGAAAAAGAGGGGGGGGAGGGAGAGAGAGAGAGAGAAAGGAAGGAAGGAGAAAGAAGAAAGGGAGAGGGAGAGAGAGAGAGAGG","61":"AGTACTTTCAACACTGCATGGCACATAGTAAGGGCACAATAAATGTTAATAATTATGATGGTGGTCATGATGATGATGATCATATGCTTATCTTCCATCCC","62":"GACTCTGTCACCCCCCGCCCCCTGGAAAAAATGCGTTTTTTGACTTAATGATATTTTCAATTGTGATGGGTTAATTGAGATATCACCCCACTGTAAGTTTA","63":"CATATCTCATATTTACAGATTCCTTCAGGGTAAGAAAACTTATGTCTTCTAGGGAAACCACTCCTTTTAAATCTATGTGATTTATCCTATAAGCCACTTAA","64":"AATTTAAAAAGTGTTAAGCACCATAGATGTGCATTTTTAGGAATAAGATGAGTTATTCACTGAAGAAGAGCTCTGCAGGAAGGTGAAAGCTCTCCTTTAAA","65":"ATGGGTTTTGGATTTAATGGGGCATTGGGGGAGTGAGAGGGCATCTGCAGAAAAGAGCCATCCAGGCTGCAGAACTCTTGTTTCCAGCAAATAGTCCATTG","66":"AGATACCAGGAATGACCTGATTCAGGCTAGTAAGTGACGTTTGCCTAGAGATCAGTCTAACTGGGGCTCAAGATATGGCCTAGCTGTGAAACAACAGATGA"},"label":{"0":[1,0,0,0,0,0,0,0,0,0,0,0],"1":[1,0,0,0,0,0,0,0,0,0,0,0],"2":[1,0,0,0,0,0,0,0,0,0,0,0],"3":[1,0,0,0,0,0,0,0,0,0,0,0],"4":[1,0,0,0,0,0,0,0,0,0,0,0],"5":[1,0,0,0,0,0,0,0,0,0,0,0],"6":[1,0,0,0,0,0,0,0,0,0,0,0],"7":[1,0,0,0,0,0,0,0,0,0,0,0],"8":[1,0,0,0,0,0,0,0,0,0,0,0],"9":[1,0,0,0,0,0,0,0,0,0,0,0],"10":[1,0,0,0,0,0,0,0,0,0,0,0],"11":[1,0,0,0,0,0,0,0,0,0,0,0],"12":[1,0,0,0,0,0,0,0,0,0,0,0],"13":[1,0,0,0,0,0,0,0,0,0,0,0],"14":[1,0,0,0,0,0,0,0,0,0,0,0],"15":[1,0,0,0,0,0,0,0,0,0,0,0],"16":[1,0,0,0,0,0,0,0,0,0,0,0],"17":[1,0,0,0,0,0,0,0,0,0,0,0],"18":[1,0,0,0,0,0,0,0,0,0,0,0],"19":[1,0,0,0,0,0,0,0,0,0,0,0],"20":[1,0,0,0,0,0,0,0,0,0,0,0],"21":[1,0,0,0,0,0,0,0,0,0,0,0],"22":[1,0,0,0,0,0,0,0,0,0,0,0],"23":[1,0,0,0,0,0,0,0,0,0,0,0],"24":[1,0,0,0,0,0,0,0,0,0,0,0],"25":[1,0,0,0,0,0,0,0,0,0,0,0],"26":[1,0,0,0,0,0,0,0,0,0,0,0],"27":[1,0,0,0,0,0,0,0,0,0,0,0],"28":[1,0,0,0,0,0,0,0,0,0,0,0],"29":[1,0,0,0,0,0,0,0,0,0,0,0],"30":[1,0,0,0,0,0,0,0,0,0,0,0],"31":[1,0,0,0,0,0,0,0,0,0,0,0],"32":[1,0,0,0,0,0,0,0,0,0,0,0],"33":[1,0,0,0,0,0,0,0,0,0,0,0],"34":[1,0,0,0,0,0,0,0,0,0,0,0],"35":[1,0,0,0,0,0,0,0,0,0,0,0],"36":[1,0,0,0,0,0,0,0,0,0,0,0],"37":[1,0,0,0,0,0,0,0,0,0,0,0],"38":[1,0,0,0,0,0,0,0,0,0,0,0],"39":[1,0,0,0,0,0,0,0,0,0,0,0],"40":[1,0,0,0,0,0,0,0,0,0,0,0],"41":[1,0,0,0,0,0,0,0,0,0,0,0],"42":[1,0,0,0,0,0,0,0,0,0,0,0],"43":[1,0,0,0,0,0,0,0,0,0,0,0],"44":[1,0,0,0,0,0,0,0,0,0,0,0],"45":[1,0,0,0,0,0,0,0,0,0,0,0],"46":[1,0,0,0,0,0,0,0,0,0,0,0],"47":[1,0,0,0,0,0,0,0,0,0,0,0],"48":[1,0,0,0,0,0,0,0,0,0,0,0],"49":[1,0,0,0,0,0,0,0,0,0,0,0],"50":[1,0,0,0,0,0,0,0,0,0,0,0],"51":[1,0,0,0,0,0,0,0,0,0,0,0],"52":[1,0,0,0,0,0,0,0,0,0,0,0],"53":[1,0,0,0,0,0,0,0,0,0,0,0],"54":[1,0,0,0,0,0,0,0,0,0,0,0],"55":[1,0,0,0,0,0,0,0,0,0,0,0],"56":[1,0,0,0,0,0,0,0,0,0,0,0],"57":[1,0,0,0,0,0,0,0,0,0,0,0],"58":[1,0,0,0,0,0,0,0,0,0,0,0],"59":[1,0,0,0,0,0,0,0,0,0,0,0],"60":[1,0,0,0,0,0,0,0,0,0,0,0],"61":[1,0,0,0,0,0,0,0,0,0,0,0],"62":[1,0,0,0,0,0,0,0,0,0,0,0],"63":[1,0,0,0,0,0,0,0,0,0,0,0],"64":[1,0,0,0,0,0,0,0,0,0,0,0],"65":[1,0,0,0,0,0,0,0,0,0,0,0],"66":[1,0,0,0,0,0,0,0,0,0,0,0]}}