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import datasets
import numpy as np
import ast
from PIL import Image
class NeuripsConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(NeuripsConfig, self).__init__(**kwargs)
class NeuripsDataset(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
NeuripsConfig(
name="Kenya",
version=datasets.Version("1.0.0"),
description="Full dataset, combining both systematically and opportunistically sampled leaves"
),
NeuripsConfig(
name="South_Africa",
version=datasets.Version("1.0.0"),
description="Subset containing only systematically sampled leaves"
),
NeuripsConfig(
name="USA_Summer",
version=datasets.Version("1.0.0"),
description="Subset containing only opportunistically sampled leaves"
),
NeuripsConfig(
name="USA_Winter",
version=datasets.Version("1.0.0"),
description="Subset containing only opportunistically sampled leaves"
),
NeuripsConfig(
name="Species_ID",
version=datasets.Version("1.0.0"),
description="Subset containing the DataFrames allowing to link the target encounter rates to a list of species for each country"
)
]
DEFAULT_CONFIG_NAME = "Kenya"
def _info(self):
#state,state_code,split,num_complete_checklists,target,geometry
if self.config.name == "Kenya":
features = datasets.Features({
"hotspot_id": datasets.Value("string"),
"hotspot_name": datasets.Value("string"),
"lon": datasets.Value("float32"),
"lat": datasets.Value("float32"),
"county": datasets.Value("string"),
"county_code": datasets.Value("string"),
"state": datasets.Value("string"),
"state_code": datasets.Value("string"),
'sat_imagery_path': datasets.Value("string"),
'environmental_path': datasets.Value("string"),
"split": datasets.Value("string"),
"num_complete_checklists" : datasets.Value("int32"),
"target": datasets.Sequence(feature=datasets.Value("float32"), length=1054),
"geometry": datasets.Value("string")
})
elif self.config.name == "South_Africa":
features = datasets.Features({
"hotspot_id": datasets.Value("string"),
"hotspot_name": datasets.Value("string"),
"lon": datasets.Value("float32"),
"lat": datasets.Value("float32"),
"county": datasets.Value("string"),
"county_code": datasets.Value("string"),
"state": datasets.Value("string"),
"state_code": datasets.Value("string"),
'sat_imagery_path': datasets.Value("string"),
'environmental_path': datasets.Value("string"),
"split": datasets.Value("string"),
"num_complete_checklists" : datasets.Value("int32"),
"target": datasets.Sequence(feature=datasets.Value("float32"), length=1054),
"geometry": datasets.Value("string")
})
elif self.config.name == "USA_Summer":
features = datasets.Features({
"hotspot_id": datasets.Value("string"),
"hotspot_name": datasets.Value("string"),
"lon": datasets.Value("float32"),
"lat": datasets.Value("float32"),
"county": datasets.Value("string"),
"county_code": datasets.Value("string"),
"state": datasets.Value("string"),
"state_code": datasets.Value("string"),
'sat_imagery_path': datasets.Value("string"),
'environmental_path': datasets.Value("string"),
"split": datasets.Value("string"),
"num_complete_checklists" : datasets.Value("int32"),
"target": datasets.Sequence(feature=datasets.Value("float32"), length=1054),
"geometry": datasets.Value("string")
})
elif self.config.name == "USA_Winter":
features = datasets.Features({
"hotspot_id": datasets.Value("string"),
"hotspot_name": datasets.Value("string"),
"lon": datasets.Value("float32"),
"lat": datasets.Value("float32"),
"county": datasets.Value("string"),
"county_code": datasets.Value("string"),
"state": datasets.Value("string"),
"state_code": datasets.Value("string"),
'sat_imagery_path': datasets.Value("string"),
'environmental_path': datasets.Value("string"),
"split": datasets.Value("string"),
"num_complete_checklists" : datasets.Value("int32"),
"target": datasets.Sequence(feature=datasets.Value("float32"), length=1054),
"geometry": datasets.Value("string")
})
elif self.config.name == "Species_ID":
features = datasets.Features({
"scientific_name": datasets.Value("string"),
"ebird_code": datasets.Value("string"),
"inat_preview": datasets.Image(),
"target_value_index" : datasets.Value("int32"),
})
else:
raise ValueError(f"Unsupported config: {self.config.name}")
return datasets.DatasetInfo(
description="The SITTELLE Benchmark Dataset",
features=features,
supervised_keys=None,
homepage="https://huggingface.co/datasets/imageomics/invasive_plants_hawaii",
license="MIT",
)
def _split_generators(self, dl_manager):
if self.config.name == "Kenya":
train_csv = "Kenya/train_split.csv"
test_csv = "Kenya/test_split.csv"
val_csv = "Kenya/valid_split.csv"
return [
datasets.SplitGenerator(name="train", gen_kwargs={"filepath": train_csv}),
datasets.SplitGenerator(name="val", gen_kwargs={"filepath": test_csv}),
datasets.SplitGenerator(name="test", gen_kwargs={"filepath": val_csv}),
]
elif self.config.name == "South_Africa":
train_csv = "Kenya/train_split.csv"
test_csv = "Kenya/test_split.csv"
val_csv = "Kenya/valid_split.csv"
return [
datasets.SplitGenerator(name="train", gen_kwargs={"filepath": train_csv}),
datasets.SplitGenerator(name="val", gen_kwargs={"filepath": test_csv}),
datasets.SplitGenerator(name="test", gen_kwargs={"filepath": val_csv}),
]
elif self.config.name == "USA_Summer":
train_csv = "Kenya/train_split.csv"
test_csv = "Kenya/test_split.csv"
val_csv = "Kenya/valid_split.csv"
return [
datasets.SplitGenerator(name="train", gen_kwargs={"filepath": train_csv}),
datasets.SplitGenerator(name="val", gen_kwargs={"filepath": test_csv}),
datasets.SplitGenerator(name="test", gen_kwargs={"filepath": val_csv}),
]
elif self.config.name == "USA_Winter":
train_csv = "Kenya/train_split.csv"
test_csv = "Kenya/test_split.csv"
val_csv = "Kenya/valid_split.csv"
return [
datasets.SplitGenerator(name="train", gen_kwargs={"filepath": train_csv}),
datasets.SplitGenerator(name="val", gen_kwargs={"filepath": test_csv}),
datasets.SplitGenerator(name="test", gen_kwargs={"filepath": val_csv}),
]
elif self.config.name == "Species_ID":
kenya_csv = "Kenya/species_id_kenya.csv"
southafrica_csv = "Kenya/species_id_kenya.csv"
usasummer_csv = "Kenya/species_id_kenya.csv"
usawinter_csv = "Kenya/species_id_kenya.csv"
return [
datasets.SplitGenerator(name="Kenya", gen_kwargs={"filepath": kenya_csv}),
datasets.SplitGenerator(name="South_Africa", gen_kwargs={"filepath": southafrica_csv}),
datasets.SplitGenerator(name="USA_Summer", gen_kwargs={"filepath": usasummer_csv}),
datasets.SplitGenerator(name="USA_Winter", gen_kwargs={"filepath": usawinter_csv}),
]
else:
raise ValueError(f"Unknown config: {self.config.name}")
def _generate_examples(self, filepath):
if self.config.name in ["Kenya", "South_Africa", "USA_Summer", "USA_Winter"]:
df_metadata = pd.read_csv(filepath)
df_metadata["target"] = df_metadata["target"].apply(ast.literal_eval).apply(lambda x: list(map(float, x)))
for idx in range(len(df_metadata)):
row = df_metadata.iloc[idx]
yield idx, {
"hotspot_id": row['hotspot_id'],
"hotspot_name": row['hotspot_name'],
"lon": row['lon'],
"lat": row['lat'],
"county": row['county'],
"county_code": row['county_code'],
"state": row['state'],
"state_code": row['state_code'],
'sat_imagery_path': row['sat_imagery_path'],
'environmental_path': row['environmental_path'],
"split": row['split'],
"num_complete_checklists" : row['num_complete_checklists'],
"target": row['target'],
"geometry": row['geometry']
}
if self.config.name == "Species_ID":
df_metadata = pd.read_csv(filepath)
for idx in range(len(df_metadata)):
row = df_metadata.iloc[idx]
yield idx, {
"scientific_name": row['scientific_name'],
"ebird_code": row['ebird_code'],
"inat_preview": row['inat_preview'],
"target_value_index" : row['target_value_index'],
} |