Upload crysmtm.py with huggingface_hub

crysmtm.py

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+"""
+CrysMTM: A Multiphase, Temperature-Resolved, Multimodal Dataset for Crystalline Materials
+"""
+
+import os
+import pandas as pd
+from datasets import Dataset, DatasetDict, Features, Value, Image, Sequence
+from PIL import Image as PILImage
+
+_CITATION = """\
+@dataset{crysmtm2024,
+  title={CrysMTM: A Multiphase, Temperature-Resolved, Multimodal Dataset for Crystalline Materials},
+  author={Can Polat and Erchin Serpedin and Mustafa Kurban and Hasan Kurban},
+  year={2024},
+  url={https://github.com/KurbanIntelligenceLab/CrysMTM}
+}
+"""
+
+_DESCRIPTION = """\
+CrysMTM is a comprehensive multiphase, temperature-resolved, multimodal dataset for crystalline materials research, 
+specifically focused on titanium dioxide (TiO₂) polymorphs. The dataset is designed primarily for regression tasks 
+to predict 9 key material properties from multimodal inputs.
+"""
+
+_HOMEPAGE = "https://github.com/KurbanIntelligenceLab/CrysMTM"
+
+_LICENSE = "cc-by-4.0"
+
+def load_crysmtm_dataset(data_dir, split="train"):
+    """Load CrysMTM dataset for a specific split."""
+    
+    # Load metadata
+    metadata_path = os.path.join(data_dir, "metadata", f"{split}_metadata.csv")
+    df = pd.read_csv(metadata_path)
+    
+    def load_example(row):
+        """Load a single example with all modalities."""
+        example = {
+            "phase": row["phase"],
+            "temperature": row["temperature"],
+            "rotation": row["rotation"],
+            "split": row["split"]
+        }
+        
+        # Load image
+        if pd.notna(row["image_path"]):
+            image_path = os.path.join(data_dir, row["image_path"])
+            if os.path.exists(image_path):
+                example["image"] = PILImage.open(image_path).convert("RGB")
+        
+        # Load XYZ coordinates
+        if pd.notna(row["xyz_path"]):
+            xyz_path = os.path.join(data_dir, row["xyz_path"])
+            if os.path.exists(xyz_path):
+                with open(xyz_path, 'r') as f:
+                    lines = f.readlines()[2:]  # Skip header lines
+                    coords = []
+                    elements = []
+                    for line in lines:
+                        parts = line.strip().split()
+                        if len(parts) >= 4:
+                            elements.append(parts[0])
+                            coords.append([float(x) for x in parts[1:4]])
+                    example["xyz_coordinates"] = coords
+                    example["elements"] = elements
+        
+        # Load text
+        if pd.notna(row["text_path"]):
+            text_path = os.path.join(data_dir, row["text_path"])
+            if os.path.exists(text_path):
+                with open(text_path, 'r') as f:
+                    example["text"] = f.read()
+        
+        # Add regression labels
+        regression_properties = ["HOMO", "LUMO", "Eg", "Ef", "Et", "Eta", "disp", "vol", "bond"]
+        example["regression_labels"] = [row[prop] for prop in regression_properties]
+        
+        # Add classification label
+        example["classification_label"] = row["label"]
+        
+        return example
+    
+    # Create dataset
+    dataset = Dataset.from_list([load_example(row) for _, row in df.iterrows()])
+    
+    return dataset
+
+def load_dataset(data_dir="."):
+    """Load the complete CrysMTM dataset."""
+    
+    splits = ["train", "test_id", "test_ood"]
+    dataset_dict = {}
+    
+    for split in splits:
+        try:
+            dataset_dict[split] = load_crysmtm_dataset(data_dir, split)
+        except FileNotFoundError:
+            print(f"Warning: {split} split not found")
+    
+    return DatasetDict(dataset_dict)
+
+# Main function for Hugging Face Hub
+def load_crysmtm():
+    """Main function to load CrysMTM dataset."""
+    return load_dataset(".")