Upload test_neurips_2025.py

test_neurips_2025.py

@@ -0,0 +1,220 @@
+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.Value("string"),
+                "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":
+            print("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'],
+                    }