test_neurips_2025.py

import pandas as pd
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'],
                    }