Datasets:
File size: 2,774 Bytes
803d2ea |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
import datasets
import pandas as pd
_CITATION = """\
Feng Q, Liang S, Jia H, et al. Gut microbiome development along the colorectal adenoma-carcinoma sequence. Nat Commun. 2015;6:6528. Published 2015 Mar 11. doi:10.1038/ncomms7528
"""
_DESCRIPTION = """\
The dataset contains 16S rRNA gene sequencing data from healthy controls and colorectal cancer patients. The dataset was used in the paper "Gut microbiome development along the colorectal adenoma-carcinoma sequence" by Feng et al. (2015).
"""
_HOMEPAGE = "https://pubmed.ncbi.nlm.nih.gov/25758642/"
_URL = "https://huggingface.co/datasets/wwydmanski/colorectal-carcinoma-microbiome-fengq/raw/main/"
class ColorectalCarcinomaMicrobiomeFengQConfig(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="presence-absence",
description="Binary presence/absence of taxa",
),
datasets.BuilderConfig(
name="CLR",
description="Relative abundance of taxa",
),
]
DEFAULT_CONFIG_NAME = "presence-absence"
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
citation=_CITATION,
homepage=_HOMEPAGE,
version=datasets.Version("0.1.0"),
)
def _split_generators(self, dl_manager):
if self.config.name == "presence-absence":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": "PA_train.csv",
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": "PA_test.csv",
"split": "test",
},
),
]
elif self.config.name == "CLR":
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": "CLR_train.csv",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": "CLR_test.csv",
},
),
]
def _generate_examples(self, filepath, split=None):
df = pd.read_csv(_URL+filepath)
for i, row in df.iterrows():
target = row["target"]
values = row.drop("target").values
yield i, {
"values": values,
"target": target,
} |