Added test.py

test.py

@@ -0,0 +1,276 @@
+import pandas as pd
+import datasets
+import numpy as np
+import ast
+from PIL import Image
+
+class InvasivePlantsConfig(datasets.BuilderConfig):
+
+    def __init__(self, **kwargs):
+        super(InvasivePlantsConfig, self).__init__(**kwargs)
+        
+class InvasivePlantsDataset(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIGS = [
+        InvasivePlantsConfig(
+            name="Kenya",
+            version=datasets.Version("1.0.0"),
+            description="Subset of the Dataset containing Hotspots from Kenya"
+        ),
+        InvasivePlantsConfig(
+            name="South_Africa",
+            version=datasets.Version("1.0.0"),
+            description="Subset of the Dataset containing Hotspots from South Africa"
+        ),
+        InvasivePlantsConfig(
+            name="USA_Summer",
+            version=datasets.Version("1.0.0"),
+            description="Subset of the Dataset containing Hotspots from the United States (Summer Season)"
+        ),
+        InvasivePlantsConfig(
+            name="USA_Winter",
+            version=datasets.Version("1.0.0"),
+            description="Subset of the Dataset containing Hotspots from the United States (Winter Season) "
+        ),
+        InvasivePlantsConfig(
+            name="Species_ID",
+            version=datasets.Version("1.0.0"),
+            description="Subset containing the list of species for each country"
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "Species_ID"
+
+    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"),
+                "lon": datasets.Value("float32"),
+                "lat": datasets.Value("float32"),
+                'sat_imagery_path': datasets.Value("string"),
+                'environmental_path': datasets.Value("string"),
+                "target_path": datasets.Value("string"),
+                "bio_1":  datasets.Value("float32"),
+                "bio_2":  datasets.Value("float32"),
+                "bio_3":  datasets.Value("float32"),
+                "bio_4":  datasets.Value("float32"),
+                "bio_5":  datasets.Value("float32"),
+                "bio_6":  datasets.Value("float32"),
+                "bio_7":  datasets.Value("float32"),
+                "bio_8":  datasets.Value("float32"),
+                "bio_9":  datasets.Value("float32"),
+                "bio_10":  datasets.Value("float32"),
+                "bio_11":  datasets.Value("float32"),
+                "bio_12":  datasets.Value("float32"),
+                "bio_13":  datasets.Value("float32"),
+                "bio_14":  datasets.Value("float32"),
+                "bio_15":  datasets.Value("float32"),
+                "bio_16":  datasets.Value("float32"),
+                "bio_17":  datasets.Value("float32"),
+                "bio_18":  datasets.Value("float32"),
+                "bio_19":  datasets.Value("float32"),
+                "num_complete_checklists" : datasets.Value("int32"),
+                "split": datasets.Value("string"),
+                })
+        elif self.config.name == "South_Africa":
+            features = datasets.Features({
+                "hotspot_id": datasets.Value("string"),
+                "lon": datasets.Value("float32"),
+                "lat": datasets.Value("float32"),
+                'sat_imagery_path': datasets.Value("string"),
+                'environmental_path': datasets.Value("string"),
+                "target_path": datasets.Value("string"),
+                "bio_1":  datasets.Value("float32"),
+                "bio_2":  datasets.Value("float32"),
+                "bio_3":  datasets.Value("float32"),
+                "bio_4":  datasets.Value("float32"),
+                "bio_5":  datasets.Value("float32"),
+                "bio_6":  datasets.Value("float32"),
+                "bio_7":  datasets.Value("float32"),
+                "bio_8":  datasets.Value("float32"),
+                "bio_9":  datasets.Value("float32"),
+                "bio_10":  datasets.Value("float32"),
+                "bio_11":  datasets.Value("float32"),
+                "bio_12":  datasets.Value("float32"),
+                "bio_13":  datasets.Value("float32"),
+                "bio_14":  datasets.Value("float32"),
+                "bio_15":  datasets.Value("float32"),
+                "bio_16":  datasets.Value("float32"),
+                "bio_17":  datasets.Value("float32"),
+                "bio_18":  datasets.Value("float32"),
+                "bio_19":  datasets.Value("float32"),
+                "num_complete_checklists" : datasets.Value("int32"),
+                "split": datasets.Value("string"),
+            })
+        elif self.config.name == "USA_Summer":
+            features = datasets.Features({
+                "hotspot_id": datasets.Value("string"),
+                "lon": datasets.Value("float32"),
+                "lat": datasets.Value("float32"),
+                'sat_imagery_path': datasets.Value("string"),
+                'environmental_path': datasets.Value("string"),
+                "target_path": datasets.Value("string"),
+                "bio_1":  datasets.Value("float32"),
+                "bio_2":  datasets.Value("float32"),
+                "bio_3":  datasets.Value("float32"),
+                "bio_4":  datasets.Value("float32"),
+                "bio_5":  datasets.Value("float32"),
+                "bio_6":  datasets.Value("float32"),
+                "bio_7":  datasets.Value("float32"),
+                "bio_8":  datasets.Value("float32"),
+                "bio_9":  datasets.Value("float32"),
+                "bio_10":  datasets.Value("float32"),
+                "bio_11":  datasets.Value("float32"),
+                "bio_12":  datasets.Value("float32"),
+                "bio_13":  datasets.Value("float32"),
+                "bio_14":  datasets.Value("float32"),
+                "bio_15":  datasets.Value("float32"),
+                "bio_16":  datasets.Value("float32"),
+                "bio_17":  datasets.Value("float32"),
+                "bio_18":  datasets.Value("float32"),
+                "bio_19":  datasets.Value("float32"),
+                "num_complete_checklists" : datasets.Value("int32"),
+                "split": datasets.Value("string"),
+            })
+        elif self.config.name == "USA_Winter":
+            features = datasets.Features({
+                "hotspot_id": datasets.Value("string"),
+                "lon": datasets.Value("float32"),
+                "lat": datasets.Value("float32"),
+                'sat_imagery_path': datasets.Value("string"),
+                'environmental_path': datasets.Value("string"),
+                "target_path": datasets.Value("string"),
+                "bio_1":  datasets.Value("float32"),
+                "bio_2":  datasets.Value("float32"),
+                "bio_3":  datasets.Value("float32"),
+                "bio_4":  datasets.Value("float32"),
+                "bio_5":  datasets.Value("float32"),
+                "bio_6":  datasets.Value("float32"),
+                "bio_7":  datasets.Value("float32"),
+                "bio_8":  datasets.Value("float32"),
+                "bio_9":  datasets.Value("float32"),
+                "bio_10":  datasets.Value("float32"),
+                "bio_11":  datasets.Value("float32"),
+                "bio_12":  datasets.Value("float32"),
+                "bio_13":  datasets.Value("float32"),
+                "bio_14":  datasets.Value("float32"),
+                "bio_15":  datasets.Value("float32"),
+                "bio_16":  datasets.Value("float32"),
+                "bio_17":  datasets.Value("float32"),
+                "bio_18":  datasets.Value("float32"),
+                "bio_19":  datasets.Value("float32"),
+                "num_complete_checklists" : datasets.Value("int32"),
+                "split": 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"),
+            })
+        else:
+            raise ValueError(f"Unsupported config: {self.config.name}")
+        
+        return datasets.DatasetInfo(
+            description="The BATIS Benchmark Dataset",
+            features=features,
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/cathv/test",
+            license="CC-BY-NC-4.0",
+        )
+    
+    def _split_generators(self, dl_manager):
+        if self.config.name == "Kenya":
+            train_csv = "Kenya/train_filtered.csv"
+            test_csv = "Kenya/test_filtered.csv" 
+            val_csv = "Kenya/valid_filtered.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 = "South_Africa/train_filtered.csv"
+            test_csv = "South_Africa/test_filtered.csv" 
+            val_csv = "South_Africa/valid_filtered.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 = "USA_Winter/train_filtered.csv"
+            test_csv = "USA_Winter/test_filtered.csv" 
+            val_csv = "USA_Winter/valid_filtered.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 = "USA_Summer/train_filtered.csv"
+            test_csv = "USA_Summer/test_filtered.csv" 
+            val_csv = "USA_Summer/valid_filtered.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 = "Species_ID/species_list_kenya.csv"
+            southafrica_csv = "Species_ID/species_list_south_africa.csv"
+            usa_csv = "Species_ID/species_list_usa.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", gen_kwargs={"filepath": usa_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)
+            for idx in range(len(df_metadata)):
+                row = df_metadata.iloc[idx]
+                yield idx, {
+                    "hotspot_id": row['hotspot_id'],
+                    "lon": row['lon'],
+                    "lat": row['lat'],
+                    'sat_imagery_path': f"images/{row['hotspot_id']}.tif",
+                    'environmental_path': f"environmental/{row['hotspot_id']}.npy",
+                    "target_path": f"targets/{row['hotspot_id']}.json",
+                    "bio_1":  row['bio_1'],
+                    "bio_2":  row['bio_2'],
+                    "bio_3":  row['bio_3'],
+                    "bio_4":  row['bio_4'],
+                    "bio_5":  row['bio_5'],
+                    "bio_6":  row['bio_6'],
+                    "bio_7":  row['bio_7'],
+                    "bio_8":  row['bio_8'],
+                    "bio_9":  row['bio_9'],
+                    "bio_10":  row['bio_10'],
+                    "bio_11":  row['bio_11'],
+                    "bio_12":  row['bio_12'],
+                    "bio_13":  row['bio_13'],
+                    "bio_14":  row['bio_14'],
+                    "bio_15":  row['bio_15'],
+                    "bio_16":  row['bio_16'],
+                    "bio_17":  row['bio_17'],
+                    "bio_18":  row['bio_18'],
+                    "bio_19":  row['bio_19'],
+                    "num_complete_checklists" : row['num_complete_checklists'],
+                    "split": row['split'],
+                }
+        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'],
+                    }