Delete test_neurips_2025.py

test_neurips_2025.py

@@ -1,218 +0,0 @@
-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"),
-                "common_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'],
-                    }