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